WorldWideScience

Sample records for adaptive sensor fusion

  1. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  2. Sensor Data Fusion

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Stepán, Petr

    2006-01-01

    The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a Sensor Data Fusion (SDF) architecture. This approach involves combined sonar array with stereo vision readings.  Sonar readings are interpreted using probability density functions...

  3. Sensor Data Fusion

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Stepán, Petr

    2006-01-01

    The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a Sensor Data Fusion (SDF) architecture. This approach involves combined sonar array with stereo vision readings.  Sonar readings are interpreted using probability density functions...... to the occupied and empty regions. Scale Invariant Feature Transform (SIFT) feature descriptors are interpreted using gaussian probabilistic error models. The use of occupancy grids is proposed for representing the sensor readings. The Bayesian estimation approach is applied to update the sonar array......  and the SIFT descriptors' uncertainty grids. The sensor fusion yields a significant reduction in the uncertainty of the occupancy grid compared to the individual sensor readings....

  4. Soldier systems sensor fusion

    Science.gov (United States)

    Brubaker, Kathryne M.

    1998-08-01

    This paper addresses sensor fusion and its applications in emerging Soldier Systems integration and the unique challenges associated with the human platform. Technology that,provides the highest operational payoff in a lightweight warrior system must not only have enhanced capabilities, but have low power components resulting in order of magnitude reductions coupled with significant cost reductions. These reductions in power and cost will be achieved through partnership with industry and leveraging of commercial state of the art advancements in microelectronics and power sources. As new generation of full solution fire control systems (to include temperature, wind and range sensors) and target acquisition systems will accompany a new generation of individual combat weapons and upgrade existing weapon systems. Advanced lightweight thermal, IR, laser and video senors will be used for surveillance, target acquisition, imaging and combat identification applications. Multifunctional sensors will provide embedded training features in combat configurations allowing the soldier to 'train as he fights' without the traditional cost and weight penalties associated with separate systems. Personal status monitors (detecting pulse, respiration rate, muscle fatigue, core temperature, etc.) will provide commanders and highest echelons instantaneous medical data. Seamless integration of GPS and dead reckoning (compass and pedometer) and/or inertial sensors will aid navigation and increase position accuracy. Improved sensors and processing capability will provide earlier detection of battlefield hazards such as mines, enemy lasers and NBC (nuclear, biological, chemical) agents. Via the digitized network the situational awareness database will automatically be updated with weapon, medical, position and battlefield hazard data. Soldier Systems Sensor Fusion will ultimately establish each individual soldier as an individual sensor on the battlefield.

  5. Sensor fusion for social robotics

    OpenAIRE

    Duffy, Brian R.; Garcia, C; Rooney, Colm, (Thesis); O'Hare, G.M.P.

    2000-01-01

    This paper advocates the application of sensor fusion for the visualisation of social robotic behaviour. Experiments with the Virtual Reality Workbench integrate the key elements of Virtual Reality and robotics in a coherent and systematic manner. The deliberative focusing of attention and sensor fusion between vision systems and sonar sensors is implemented on autonomous mobile robots functioning in standard office environments

  6. Multi-sensor fusion using an adaptive multi-hypothesis tracking algorithm

    NARCIS (Netherlands)

    Kester, L.J.H.M.

    2003-01-01

    The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the

  7. Data Fusion and Sensors Model

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide our idea on the relation of sensor model, data fusion and system structure, and in the paper, there is a solution that transform the parameter space to get linear model for Kalman filter.

  8. Multi-sensor fusion development

    Science.gov (United States)

    Bish, Sheldon; Rohrer, Matthew; Scheffel, Peter; Bennett, Kelly

    2016-05-01

    The U.S. Army Research Laboratory (ARL) and McQ Inc. are developing a generic sensor fusion architecture that involves several diverse processes working in combination to create a dynamic task-oriented, real-time informational capability. Processes include sensor data collection, persistent and observational data storage, and multimodal and multisensor fusion that includes the flexibility to modify the fusion program rules for each mission. Such a fusion engine lends itself to a diverse set of sensing applications and architectures while using open-source software technologies. In this paper, we describe a fusion engine architecture that combines multimodal and multi-sensor fusion within an Open Standard for Unattended Sensors (OSUS) framework. The modular, plug-and-play architecture of OSUS allows future fusion plugin methodologies to have seamless integration into the fusion architecture at the conceptual and implementation level. Although beyond the scope of this paper, this architecture allows for data and information manipulation and filtering for an array of applications.

  9. Desensitized Optimal Filtering and Sensor Fusion Toolkit

    Science.gov (United States)

    Karlgaard, Christopher D.

    2015-01-01

    Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

  10. Perceptual reasoning in adaptive fusion processing

    Science.gov (United States)

    Kadar, Ivan

    2002-07-01

    The author previously published a unified perceptual reasoning system framework for adaptive sensor fusion and situation assessment. Ths framework is re-examined to highlight the role of human perceptual reasoning and to establish the relationship between human perceptual reasoning and the Joint Director of Laboratories (JDL) fusion levels. Mappings between the fusion levels and the elements of perceptual reasoning are defined. Methods to populate the knowledge bases associated with each component of the perceptual reasoning system are highlighted. The concept and application of perception, the resultant system architecture and its candidate renditions using distributed interacting software agents (ISA) are discussed. The perceptual reasoning system is shown to be a natural governing mechanism for extracting, associating and fusing information from multiple sources while adaptively controlling the fusion level processes for optimum fusion performance. The unified modular system construct is shown to provide a formal framework to accommodate various implementation alternatives. The application of this architectural concept is illustrated for distributed fusion systems architectures and is sued to illustrate the benefits of the adaptive perceptual reasoning system concept.

  11. Presence detection under optimum fusion in an ultrasonic sensor system.

    Science.gov (United States)

    Srinivasan, Sriram; Pandharipande, Ashish

    2012-04-01

    Reliable presence detection is a requirement in energy-efficient occupancy-adaptive indoor lighting systems. A system of multiple ultrasonic sensors is considered for presence detection, and the performance gain from optimum fusion is studied. Two cases are considered wherein an individual sensor determines presence based on (i) local detection by processing echoes at its receiver, and (ii) the optimum Chair-Varshney fusion rule using multiple sensor detection results. The performance gains of using optimum fusion over local detection are characterized under different sensor system configurations and it is shown that improved detection sensitivity is obtained over a larger detection coverage region.

  12. Optimal Fusion of Sensors

    DEFF Research Database (Denmark)

    Larsen, Thomas Dall

    within some global frame of reference using a wide variety of sensors providing odometric, inertial and absolute data concerning the robot and its surroundings. Kalman filters have for a long time been widely used to solve this problem. However, when measurements are delayed or the mobile robot...

  13. A novel fuzzy sensor fusion algorithm

    Institute of Scientific and Technical Information of China (English)

    FU Hua; YANG Yi-kui; MA Ke; LIU Yu-jia

    2011-01-01

    A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory.First,it calculated the fuzzy similarity among a certain sensor's measurement values and the multiple sensors' objective prediction values to determine the importance weight of each sensor and realize multi-sensor data fusion.Then according to the determined importance weight,an intelligent fusion system based on fuzzy integral theory was given,which can solve FEI-DEO and DEI-DEO fusion problems and realize the decision fusion.Simulation results were proved that fuzzy integral algorithm has enhanced the capability of handling the uncertain information and improved the intelligence degrees.

  14. Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

    Directory of Open Access Journals (Sweden)

    José Jair Alves Mendes Jr.

    2016-09-01

    Full Text Available The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports, it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.

  15. Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

    Science.gov (United States)

    Mendes, José Jair Alves; Vieira, Mário Elias Marinho; Pires, Marcelo Bissi; Stevan, Sergio Luiz

    2016-01-01

    The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others. PMID:27669260

  16. Exploiting Real-Time FPGA Based Adaptive Systems Technology for Real-Time Sensor Fusion in Next Generation Automotive Safety Systems

    CERN Document Server

    Chappell, Steve; Preston, Dan; Olmstead, Dave; Flint, Bob; Sullivan, Chris

    2011-01-01

    We present a system for the boresighting of sensors using inertial measurement devices as the basis for developing a range of dynamic real-time sensor fusion applications. The proof of concept utilizes a COTS FPGA platform for sensor fusion and real-time correction of a misaligned video sensor. We exploit a custom-designed 32-bit soft processor core and C-based design & synthesis for rapid, platform-neutral development. Kalman filter and sensor fusion techniques established in advanced aviation systems are applied to automotive vehicles with results exceeding typical industry requirements for sensor alignment. Results of the static and the dynamic tests demonstrate that using inexpensive accelerometers mounted on (or during assembly of) a sensor and an Inertial Measurement Unit (IMU) fixed to a vehicle can be used to compute the misalignment of the sensor to the IMU and thus vehicle. In some cases the model predications and test results exceeded the requirements by an order of magnitude with a 3-sigma or ...

  17. Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

    Science.gov (United States)

    Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

    2014-07-03

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  18. Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

    Directory of Open Access Journals (Sweden)

    Shahina Begum

    2014-07-01

    Full Text Available Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR, Finger Temperature (FT, Respiration Rate (RR, Carbon dioxide (CO2 and Oxygen Saturation (SpO2 are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i decision-level fusion using features extracted through traditional approaches; and (ii data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE. Case-Based Reasoning (CBR is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  19. Sensor fusion for airborne landmine detection

    Science.gov (United States)

    Schatten, Miranda A.; Gader, Paul D.; Bolton, Jeremy; Zare, Alina; Mendez-Vasquez, Andres

    2006-05-01

    Sensor fusion has become a vital research area for mine detection because of the countermine community's conclusion that no single sensor is capable of detecting mines at the necessary detection and false alarm rates over a wide variety of operating conditions. The U. S. Army Night Vision and Electronic Sensors Directorate (NVESD) evaluates sensors and algorithms for use in a multi-sensor multi-platform airborne detection modality. A large dataset of hyperspectral and radar imagery exists from the four major data collections performed at U. S. Army temperate and arid testing facilities in Autumn 2002, Spring 2003, Summer 2004, and Summer 2005. There are a number of algorithm developers working on single-sensor algorithms in order to optimize feature and classifier selection for that sensor type. However, a given sensor/algorithm system has an absolute limitation based on the physical phenomena that system is capable of sensing. Therefore, we perform decision-level fusion of the outputs from single-channel algorithms and we choose to combine systems whose information is complementary across operating conditions. That way, the final fused system will be robust to a variety of conditions, which is a critical property of a countermine detection system. In this paper, we present the analysis of fusion algorithms on data from a sensor suite consisting of high frequency radar imagery combined with hyperspectral long-wave infrared sensor imagery. The main type of fusion being considered is Choquet integral fusion. We evaluate performance achieved using the Choquet integral method for sensor fusion versus Boolean and soft "and," "or," mean, or majority voting.

  20. Perceptual reasoning managed situation assessment and adaptive fusion processing

    Science.gov (United States)

    Kadar, Ivan

    2001-08-01

    A unified perceptual reasoning system framework for adaptive sensor fusion and situation assessment is presented. The concept and application of perception, the resultant system architecture and its candidate renditions using knowledge- based systems and associative memory are discussed. The perceptual reasoning system is shown to be a natural governing mechanism for extracting, associating and fusing information from multiple sources while adaptively controlling the Joint Director of Laboratories (JDL) Fusion Model processes for optimum fusion system performance. The unified modular system construct is shown to provide a formal framework to accommodate various implementation alternatives. The application of this architectural concept is illustrated for representative network centric surveillance system architecture. A target identification system using Dempster-Shafer declarations level fusion is used to demonstrate the benefits of the adaptive perceptual reasoning system and the iterative evidential reasoning method.

  1. Optimal decision fusion given sensor rules

    Institute of Scientific and Technical Information of China (English)

    Yunmin ZHU; Xiaorong LI

    2005-01-01

    When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities,can be derived for very general decision systems.They include those systems with interdependent sensor observations and any network structure.It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion.Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented,which take the form of a generalized likelihood ratio test.Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.

  2. Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

    Science.gov (United States)

    Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M.; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T.J.

    2010-01-01

    In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted. PMID:22294927

  3. Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

    Directory of Open Access Journals (Sweden)

    Aníbal Ollero

    2010-03-01

    Full Text Available In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites, a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

  4. An Alternate View Of Munition Sensor Fusion

    Science.gov (United States)

    Mayersak, J. R.

    1988-08-01

    An alternate multimode sensor fusion scheme is treated. The concept is designed to acquire and engage high value relocatable targets in a lock-on-after-launch sequence. The approach uses statistical decision concepts to determine the authority to be assigned to each mode in the acquisition sequence voting and decision process. Statistical target classification and recognition in the engagement sequence is accomplished through variable length feature vectors set by adaptive logics. The approach uses multiple decision for acquisition and classification, in the number of spaces selected, is adaptively weighted and adjusted. The scheme uses type of climate -- arctic, temperate, desert, and equatorial -- diurnal effects --- time of day -- type of background, type of countermeasures present -- signature suppresssion or obscuration, false target decoy or electronic warfare -- and other factors to make these selections. The approach is discussed in simple terms. Voids and deficiencies in the statistical data base used to train such algorithms is discussed. The approach is being developed to engage deep battle targets such as surface-to-surface missile systems, air defense units and self-propelled artillery.

  5. Adaptive fusion of infrared and visible images in dynamic scene

    Science.gov (United States)

    Yang, Guang; Yin, Yafeng; Man, Hong; Desai, Sachi

    2011-11-01

    Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years to improve human visual perception for object detection. One of the main challenges for visible and infrared image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an acceptable computational cost. This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations. After that, the region surrounding the target area is segmented as the background regions. Then image fusion is locally applied on the selected target and background regions by computing different linear combination of color components from registered visible and infrared images. After obtaining different fused images, histogram distributions are computed on these local fusion images as the fusion feature set. The variance ratio which is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate that our proposed method achieved a competitive performance compared with other fusion algorithms at a relatively low computational cost.

  6. Fusion of Noisy Multi-sensor Imagery

    Directory of Open Access Journals (Sweden)

    Anima Mishra

    2008-01-01

    Full Text Available Interest in fusing multiple sensor data for both military and civil applications has beengrowing. Some of the important applications integrate image information from multiple sensorsto aid in navigation guidance, object detection and recognition, medical diagnosis, datacompression, etc. While, human beings may visually inspect various images and integrateinformation, it is of interest to develop algorithms that can fuse various input imagery to producea composite image. Fusion of images from various sensor modalities is expected to produce anoutput that captures all the relevant information in the input. The standard multi-resolution-based edge fusion scheme has been reviewed in this paper. A theoretical framework is given forthis edge fusion method by showing how edge fusion can be framed as information maximisation.However, the presence of noise complicates the situation. The framework developed is used toshow that for noisy images, all edges no longer correspond to information. In this paper, varioustechniques have been presented for fusion of noisy multi-sensor images.  These techniques aredeveloped for a single resolution as well as using multi-resolution decomposition. Some of thetechniques are based on modifying edge maps by filtering images, while others depend onalternate definition of information maps. Both these approaches can also be combined.Experiments show that the proposed algorithms work well for various kinds of noisy multi-sensor images.

  7. Extending lifetime of wireless sensor networks using multi-sensor data fusion

    Indian Academy of Sciences (India)

    SOUMITRA DAS; S BARANI; SANJEEV WAGH; S S SONAVANE

    2017-07-01

    In this paper a multi-sensor data fusion approach for wireless sensor network based on bayesian methods and ant colony optimization techniques has been proposed. In this method, each node is equipped with multiple sensors (i.e., temperature and humidity). Use of more than one sensor provides additional information about the environmental conditions. The data fusion approach based on the competitive-type hierarchical processing is considered for experimentation. Initially the data are collected by the sensors placed in the sensing fields and then the data fusion probabilities are computed on the sensed data. In this proposed methodology, the collected temperature tand humidity data are processed by multi-sensor data fusion techniques, which help in decreasing the energy consumption as well as communication cost by fusing the redundant data. The multipledata fusion process improves the reliability and accuracy of the sensed information and simultaneously saves energy, which was our primary objective. The proposed algorithms were simulated using Matlab. The executions of proposed arnd low-energy adaptive clustering hierarchy algorithms were carried out and the results show that the proposed algorithms could efficiently reduce the use of energy and were able to save more energy, thus increasing the overall network lifetime.

  8. Sensor Fusion for Autonomous Mobile Robot Navigation

    DEFF Research Database (Denmark)

    Plascencia, Alfredo

    Multi-sensor data fusion is a broad area of constant research which is applied to a wide variety of fields such as the field of mobile robots. Mobile robots are complex systems where the design and implementation of sensor fusion is a complex task. But research applications are explored constantly....... The scope of the thesis is limited to building a map for a laboratory robot by fusing range readings from a sonar array with landmarks extracted from stereo vision images using the (Scale Invariant Feature Transform) SIFT algorithm....

  9. Health-Enabled Smart Sensor Fusion Technology

    Science.gov (United States)

    Wang, Ray

    2012-01-01

    A process was designed to fuse data from multiple sensors in order to make a more accurate estimation of the environment and overall health in an intelligent rocket test facility (IRTF), to provide reliable, high-confidence measurements for a variety of propulsion test articles. The object of the technology is to provide sensor fusion based on a distributed architecture. Specifically, the fusion technology is intended to succeed in providing health condition monitoring capability at the intelligent transceiver, such as RF signal strength, battery reading, computing resource monitoring, and sensor data reading. The technology also provides analytic and diagnostic intelligence at the intelligent transceiver, enhancing the IEEE 1451.x-based standard for sensor data management and distributions, as well as providing appropriate communications protocols to enable complex interactions to support timely and high-quality flow of information among the system elements.

  10. Maneuvering Vehicle Tracking Based on Multi-sensor Fusion

    Institute of Scientific and Technical Information of China (English)

    CHENYing; HANChong-Zhao

    2005-01-01

    Maneuvering targets tracking is a fundamental task in intelligent vehicle research. This paper focuses on the problem of fusion between radar and image sensors in targets tracking. In order to improve positioning accuracy and narrow down the image working area, a novel method that integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdorff distance is introduced to define the functional relationship between image and model projection, and a modified simulated annealing algorithm is used to find optimum orientation parameter. Furthermore, the global state is estimated, which refers to the distributed data fusion algorithm. Experiment results show that our method is accurate.

  11. HEAT Sensor: Harsh Environment Adaptable Thermionic Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Limb, Scott J. [Palo Alto Research Center, Palo Alto, CA (United States)

    2016-05-31

    This document is the final report for the “HARSH ENVIRONMENT ADAPTABLE THERMIONIC SENSOR” project under NETL’s Crosscutting contract DE-FE0013062. This report addresses sensors that can be made with thermionic thin films along with the required high temperature hermetic packaging process. These sensors can be placed in harsh high temperature environments and potentially be wireless and self-powered.

  12. Sensor fusion method for machine performance enhancement

    Energy Technology Data Exchange (ETDEWEB)

    Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C.; Hillaire, R. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center; Jones, S.; Furness, R. [Ford Motor Co., Dearborn, MI (United States)

    1998-03-01

    A sensor fusion methodology was developed to uniquely integrate pre-process, process-intermittent, and post-process measurement and analysis technology to cost-effectively enhance the accuracy and capability of computer-controlled manufacturing equipment. Empirical models and computational algorithms were also developed to model, assess, and then enhance the machine performance.

  13. Driver drowsiness detection using multimodal sensor fusion

    Science.gov (United States)

    Andreeva, Elena O.; Aarabi, Parham; Philiastides, Marios G.; Mohajer, Keyvan; Emami, Majid

    2004-04-01

    This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple sensors, the driver"s body is studied as a mechanical structure of springs and dampeners. The sleep-detection system consists of highly sensitive triple-axial accelerometers to monitor the driver"s upper body in 3-D. The subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake individuals for the awake state. When trained and tested on the same subject, the system detected sleep and awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested on a fourth "unseen" subject, the classification rate dropped to 90%. Furthermore, it was attempted to correlate driver posture and sleepiness by observing how car vibrations propagate through a person"s body. Eight additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive which was attributed to significant differences in the individual habitual postures.

  14. City Data Fusion: Sensor Data Fusion in the Internet of Things

    OpenAIRE

    2015-01-01

    Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework....

  15. Adaptive reconfigurable distributed sensor architecture

    Science.gov (United States)

    Akey, Mark L.

    1997-07-01

    The infancy of unattended ground based sensors is quickly coming to an end with the arrival of on-board GPS, networking, and multiple sensing capabilities. Unfortunately, their use is only first-order at best: GPS assists with sensor report registration; networks push sensor reports back to the warfighter and forwards control information to the sensors; multispectral sensing is a preset, pre-deployment consideration; and the scalability of large sensor networks is questionable. Current architectures provide little synergy among or within the sensors either before or after deployment, and do not map well to the tactical user's organizational structures and constraints. A new distributed sensor architecture is defined which moves well beyond single sensor, single task architectures. Advantages include: (1) automatic mapping of tactical direction to multiple sensors' tasks; (2) decentralized, distributed management of sensor resources and tasks; (3) software reconfiguration of deployed sensors; (4) network scalability and flexibility to meet the constraints of tactical deployments, and traditional combat organizations and hierarchies; and (5) adaptability to new battlefield communication paradigms such as BADD (Battlefield Analysis and Data Dissemination). The architecture is supported in two areas: a recursive, structural definition of resource configuration and management via loose associations; and a hybridization of intelligent software agents with tele- programming capabilities. The distributed sensor architecture is examined within the context of air-deployed ground sensors with acoustic, communication direction finding, and infra-red capabilities. Advantages and disadvantages of the architecture are examined. Consideration is given to extended sensor life (up to 6 months), post-deployment sensor reconfiguration, limited on- board sensor resources (processor and memory), and bandwidth. It is shown that technical tasking of the sensor suite can be automatically

  16. Sensor fusion for improved indoor navigation

    Science.gov (United States)

    Emilsson, Erika; Rydell, Joakim

    2012-09-01

    A reliable indoor positioning system providing high accuracy has the potential to increase the safety of first responders and military personnel significantly. To enable navigation in a broad range of environments and obtain more accurate and robust positioning results, we propose a multi-sensor fusion approach. We describe and evaluate a positioning system, based on sensor fusion between a foot-mounted inertial measurement unit (IMU) and a camera-based system for simultaneous localization and mapping (SLAM). The complete system provides accurate navigation in many relevant environments without depending on preinstalled infrastructure. The camera-based system uses both inertial measurements and visual data, thereby enabling navigation also in environments and scenarios where one of the sensors provides unreliable data during a few seconds. When sufficient light is available, the camera-based system generally provides good performance. The foot-mounted system provides accurate positioning when distinct steps can be detected, e.g., during walking and running, even in dark or smoke-filled environments. By combining the two systems, the integrated positioning system can be expected to enable accurate navigation in almost all kinds of environments and scenarios. In this paper we present results from initial tests, which show that the proposed sensor fusion improves the navigation solution considerably in scenarios where either the foot-mounted or camera-based system is unable to navigate on its own.

  17. Application of Multi-Sensors Information Fusion for Self-protection System of Robot

    Directory of Open Access Journals (Sweden)

    Qiuhong Gao

    2013-01-01

    Full Text Available This paper developed a kind of robot self-protection system using the multi-sensors information fusion technology. This system used five groups of photoelectric sensor and ultrasonic sensor which were installed in different direction of the robot. In this study, signals were gathered by using the complement of ranging of photoelectric sensor and ultrasonic sensor. Then the signals were sent to MCU to achieve multi-sensors information fusion.Core fusion technology was the adaptive weighted fusion estimation algorithm, which can make measurement data more accurate. With such technology, an accurate robot self-protection command was made as to avoid obstacles, to judge narrow highland and to prevent dropping. The experiment results validated its good self-protection function.

  18. Multimodal Sensor Fusion for Personnel Detection

    Science.gov (United States)

    2011-07-01

    Multimodal Sensor Fusion for Personnel Detection Xin Jin Shalabh Gupta Asok Ray Department of Mechanical Engineering The Pennsylvania State...have con- sidered relations taken only two at a time, but we propose to explore relations between higher order cliques as future work. D. Feature...detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 577–589, 2001. [11] A. Ray , “Symbolic dynamic analysis

  19. Non-verbal communication through sensor fusion

    Science.gov (United States)

    Tairych, Andreas; Xu, Daniel; O'Brien, Benjamin M.; Anderson, Iain A.

    2016-04-01

    When we communicate face to face, we subconsciously engage our whole body to convey our message. In telecommunication, e.g. during phone calls, this powerful information channel cannot be used. Capturing nonverbal information from body motion and transmitting it to the receiver parallel to speech would make these conversations feel much more natural. This requires a sensing device that is capable of capturing different types of movements, such as the flexion and extension of joints, and the rotation of limbs. In a first embodiment, we developed a sensing glove that is used to control a computer game. Capacitive dielectric elastomer (DE) sensors measure finger positions, and an inertial measurement unit (IMU) detects hand roll. These two sensor technologies complement each other, with the IMU allowing the player to move an avatar through a three-dimensional maze, and the DE sensors detecting finger flexion to fire weapons or open doors. After demonstrating the potential of sensor fusion in human-computer interaction, we take this concept to the next level and apply it in nonverbal communication between humans. The current fingerspelling glove prototype uses capacitive DE sensors to detect finger gestures performed by the sending person. These gestures are mapped to corresponding messages and transmitted wirelessly to another person. A concept for integrating an IMU into this system is presented. The fusion of the DE sensor and the IMU combines the strengths of both sensor types, and therefore enables very comprehensive body motion sensing, which makes a large repertoire of gestures available to nonverbal communication over distances.

  20. Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

    Science.gov (United States)

    Schenker, Paul S.

    1992-11-01

    Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

  1. Sensor data fusion to predict multiple soil properties

    NARCIS (Netherlands)

    Mahmood, H.S.; Hoogmoed, W.B.; Henten, van E.J.

    2012-01-01

    The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources.

  2. Intelligent processing techniques for sensor fusion

    Science.gov (United States)

    Byrd, Katherine A.; Smith, Bart; Allen, Doug; Morris, Norman; Bjork, Charles A., Jr.; Deal-Giblin, Kim; Rushing, John A.

    1998-03-01

    Intelligent processing techniques which can effectively combine sensor data from disparate sensors by selecting and using only the most beneficial individual sensor data is a critical element of exoatmospheric interceptor systems. A major goal of these algorithms is to provide robust discrimination against stressing threats in poor a priori conditions, and to incorporate adaptive approaches in off- nominal conditions. This paper summarizes the intelligent processing algorithms being developed, implemented and tested to intelligently fuse data from passive infrared and active LADAR sensors at the measurement, feature and decision level. These intelligent algorithms employ dynamic selection of individual sensors features and the weighting of multiple classifier decisions to optimize performance in good a priori conditions and robustness in poor a priori conditions. Features can be dynamically selected based on an estimate of the feature confidence which is determined from feature quality and weighting terms derived from the quality of sensor data and expected phenomenology. Multiple classifiers are employed which use both fuzzy logic and knowledge based approaches to fuse the sensor data and to provide a target lethality estimate. Target designation decisions can be made by fusing weighted individual classifier decisions whose output contains an estimate of the confidence of the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data or to request additional sensor data on specific objects that have not been confidently identified as being lethal or non- lethal. The algorithms are implemented in C within a graphic user interface framework. Dynamic memory allocation and the sequentialy implementation of the feature algorithms are employed. The baseline set of fused sensor discrimination algorithms with intelligent processing are described in this paper. Example results

  3. Desensitized Optimal Filtering and Sensor Fusion Tool Kit Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Research on desensitized optimal filtering techniques and a navigation and sensor fusion tool kit using advanced filtering techniques is proposed. Research focuses...

  4. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

    Directory of Open Access Journals (Sweden)

    Yu Gu

    2016-01-01

    Full Text Available A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.

  5. The ABC Adaptive Fusion Architecture

    DEFF Research Database (Denmark)

    Bunde-Pedersen, Jonathan; Mogensen, Martin; Bardram, Jakob Eyvind

    2006-01-01

    Contemporary distributed collaborative systems tend to utilizeeither a client-server or a pure peer-to-peer paradigm. Aclient-server solution may potentially spawn direct connectionsbetween the clients to offload the server thereby creatinga hybrid architecture. A pure peer-to-peer paradigmmay...... on the other hand fully eliminate the need for a server.However, some situations call for the strengths of both approacheswithout relying on either of them. A system mightboth be used in environments where an infrastructure ispresent and in environments where it is not. In this paper wepresent an architecture...... and early implementation of a systemcapable of adapting to its operating environment, choosingthe best fit combination of the client-server and peerto-peer architectures. The architecture creates a seamlessintegration between a centralized hybrid architecture and adecentralized architecture, relying on what...

  6. Sensor Fusion of Force and Acceleration for Robot Force Control

    OpenAIRE

    Gámez García, Javier; Robertsson, Anders; Gómez Ortega, Juan; Johansson, Rolf

    2004-01-01

    In this paper, robotic sensor fusion of acceleration and force measurement is considered. We discuss the problem of using accelerometers close to the end-effectors of robotic manipulators and how it may improve the force control performance. We introduce a new model-based observer approach to sensor fusion of information from various different sensors. During contact transition, accelerometers and force sensors play a very important role and it can overcome many of the difficulties of uncerta...

  7. Fluorescent sensors based on bacterial fusion proteins

    Science.gov (United States)

    Prats Mateu, Batirtze; Kainz, Birgit; Pum, Dietmar; Sleytr, Uwe B.; Toca-Herrera, José L.

    2014-06-01

    Fluorescence proteins are widely used as markers for biomedical and technological purposes. Therefore, the aim of this project was to create a fluorescent sensor, based in the green and cyan fluorescent protein, using bacterial S-layers proteins as scaffold for the fluorescent tag. We report the cloning, expression and purification of three S-layer fluorescent proteins: SgsE-EGFP, SgsE-ECFP and SgsE-13aa-ECFP, this last containing a 13-amino acid rigid linker. The pH dependence of the fluorescence intensity of the S-layer fusion proteins, monitored by fluorescence spectroscopy, showed that the ECFP tag was more stable than EGFP. Furthermore, the fluorescent fusion proteins were reassembled on silica particles modified with cationic and anionic polyelectrolytes. Zeta potential measurements confirmed the particle coatings and indicated their colloidal stability. Flow cytometry and fluorescence microscopy showed that the fluorescence of the fusion proteins was pH dependent and sensitive to the underlying polyelectrolyte coating. This might suggest that the fluorescent tag is not completely exposed to the bulk media as an independent moiety. Finally, it was found out that viscosity enhanced the fluorescence intensity of the three fluorescent S-layer proteins.

  8. Advances in multi-sensor data fusion: algorithms and applications.

    Science.gov (United States)

    Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

    2009-01-01

    With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

  9. Tracking and sensor data fusion methodological framework and selected applications

    CERN Document Server

    Koch, Wolfgang

    2013-01-01

    Sensor Data Fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying real-world phenomenon is achieved. Typically, this insight is either unobtainable otherwise or a fusion result exceeds what can be produced from a single sensor output in accuracy, reliability, or cost. This book provides an introduction Sensor Data Fusion, as an information technology as well as a branch of engineering science and informatics. Part I presents a coherent methodological framework, thus providing th

  10. Distributed fusion and automated sensor tasking in ISR systems

    Science.gov (United States)

    Preden, Jurgo; Pahtma, Raido; Astapov, Sergei; Ehala, Johannes; Riid, Andri; Motus, Leo

    2014-06-01

    Modern Intelligence, Surveillance and Reconnaissance (ISR) systems are increasingly being assembled from autonomous systems, so the resulting ISR system is a System of Systems (SoS). In order to take full advantage of the capabilities of the ISR SoS, the architecture and the design of these SoS should be able to facilitate the benefits inherent in a SoS approach - high resilience, higher level of adaptability and higher diversity, enabling on-demand system composition. The tasks performed by ISR SoS can well go beyond basic data acquisition, conditioning and communication as data processing can be easily integrated in the SoS. Such an ISR SoS can perform data fusion, classification and tracking (and conditional sensor tasking for additional data acquisition), these are extremely challenging tasks in this context, especially if the fusion is performed in a distributed manner. Our premise for the ISR SoS design and deployment is that the system is not designed as a complete system, where the capabilities of individual data providers are considered and the interaction paths, including communication channel capabilities, are specified at design time. Instead, we assume a loosely coupled SoS, where the data needs for a specific fusion task are described at a high level at design time and data providers (i.e., sensor systems) required for a specific fusion task are discovered dynamically at run time, the selection criteria for the data providers being the type and properties of data that can be provided by the specific data provider. The paper describes some of the aspects of a distributed ISR SoS design and implementation, bringing examples on both architectural design as well as on algorithm implementations.

  11. Sensor fusion for intelligent process control.

    Energy Technology Data Exchange (ETDEWEB)

    Connors, John J. (PPG Industries, Inc., Harmar Township, PA); Hill, Kevin (PPG Industries, Inc., Harmar Township, PA); Hanekamp, David (PPG Industries, Inc., Harmar Township, PA); Haley, William F. (PPG Industries, Inc., Wichita Falls, TX); Gallagher, Robert J.; Gowin, Craig (PPG Industries, Inc., Batavia, IL); Farrar, Arthur R. (PPG Industries, Inc., Wichita Falls, TX); Sheaffer, Donald A.; DeYoung, Mark A. (PPG Industries, Inc., Mt. Zion, IL); Bertram, Lee A.; Dodge, Craig (PPG Industries, Inc., Mt. Zion, IL); Binion, Bruce (PPG Industries, Inc., Mt. Zion, IL); Walsh, Peter M.; Houf, William G.; Desam, Padmabhushana R. (University of Utah, Salt Lake City, UT); Tiwary, Rajiv (PPG Industries, Inc., Harmar Township, PA); Stokes, Michael R. (PPG Industries, Inc.); Miller, Alan J. (PPG Industries, Inc., Mt. Zion, IL); Michael, Richard W. (PPG Industries, Inc., Lincoln, AL); Mayer, Raymond M. (PPG Industries, Inc., Harmar Township, PA); Jiao, Yu (PPG Industries, Inc., Harmar Township, PA); Smith, Philip J. (University of Utah, Salt Lake City, UT); Arbab, Mehran (PPG Industries, Inc., Harmar Township, PA); Hillaire, Robert G.

    2004-08-01

    An integrated system for the fusion of product and process sensors and controls for production of flat glass was envisioned, having as its objective the maximization of throughput and product quality subject to emission limits, furnace refractory wear, and other constraints. Although the project was prematurely terminated, stopping the work short of its goal, the tasks that were completed show the value of the approach and objectives. Though the demonstration was to have been done on a flat glass production line, the approach is applicable to control of production in the other sectors of the glass industry. Furthermore, the system architecture is also applicable in other industries utilizing processes in which product uniformity is determined by ability to control feed composition, mixing, heating and cooling, chemical reactions, and physical processes such as distillation, crystallization, drying, etc. The first phase of the project, with Visteon Automotive Systems as industrial partner, was focused on simulation and control of the glass annealing lehr. That work produced the analysis and computer code that provide the foundation for model-based control of annealing lehrs during steady state operation and through color and thickness changes. In the second phase of the work, with PPG Industries as the industrial partner, the emphasis was on control of temperature and combustion stoichiometry in the melting furnace, to provide a wider operating window, improve product yield, and increase energy efficiency. A program of experiments with the furnace, CFD modeling and simulation, flow measurements, and sensor fusion was undertaken to provide the experimental and theoretical basis for an integrated, model-based control system utilizing the new infrastructure installed at the demonstration site for the purpose. In spite of the fact that the project was terminated during the first year of the second phase of the work, the results of these first steps toward implementation

  12. PERSON AUTHENTICATION USING MULTIPLE SENSOR DATA FUSION

    Directory of Open Access Journals (Sweden)

    S. Vasuhi

    2011-04-01

    Full Text Available This paper proposes a real-time system for face authentication, obtained through fusion of Infra Red (IR and visible images. In order to identify the unknown person authentication in highly secured areas, multiple algorithms are needed. The four well known algorithms for face recognition, Block Independent Component Analysis(BICA, Kalman Filtering(KF method, Discrete Cosine Transform(DCT and Orthogonal Locality Preserving Projections (OLPP are used to extract the features. If the data base size is very large and the features are not distinct then ambiguity will exists in face recognition. Hence more than one sensor is needed for critical and/or highly secured areas. This paper deals with multiple fusion methodology using weighted average and Fuzzy Logic. The visible sensor output depends on the environmental condition namely lighting conditions, illumination etc., to overcome this problem use histogram technique to choose appropriate algorithm. DCT and Kalman filtering are holistic approaches, BICA follows feature based approach and OLPP preserves the Euclidean structure of face space. These recognizers are capable of considering the problem of dimensionality reduction by eliminating redundant features and reducing the feature space. The system can handle variations like illumination, pose, orientation, occlusion, etc. up to a significant level. The integrated system overcomes the drawbacks of individual recognizers. The proposed system is aimed at increasing the accuracy of the person authentication system and at the same time reducing the limitations of individual algorithms. It is tested on real time database and the results are found to be 96% accurate.

  13. Sensor Fusion and Model Verification for a Mobile Robot

    OpenAIRE

    Bisgaard, Morten; Vinther, Dennis; Østergaard, Kasper Zinck; Bendtsen, Jan Dimon; Izadi-Zamanabadi, Roozbeh

    2005-01-01

    This paper presents the results of modeling, sensor fusion and model verification for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The model derived for the robot describes the actuator and wheel dynamics and the vehicle kinematics, and includes friction terms as well as slip. An Unscented Kalman Filter (UKF) based on the dynamic model is used for sensor fusion, feeding sensor measurements back to the robot controller in an intelligent manner. Through practi...

  14. Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

    OpenAIRE

    Marwah Almasri; Khaled Elleithy; Abrar Alajlan

    2015-01-01

    Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot...

  15. Advances in Multi-Sensor Data Fusion: Algorithms and Applications

    Directory of Open Access Journals (Sweden)

    Jingying Fu

    2009-09-01

    Full Text Available With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1 Improvements of fusion algorithms; (2 Development of “algorithm fusion” methods; (3 Establishment of an automatic quality assessment scheme.

  16. Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

    Science.gov (United States)

    Fink, Wolfgang; George, Thomas; Tarbell, Mark A.

    2007-04-01

    Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability.

  17. Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

    Directory of Open Access Journals (Sweden)

    Shanglu He

    2016-01-01

    Full Text Available Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state, suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

  18. Context-aided sensor fusion for enhanced urban navigation.

    Science.gov (United States)

    Martí, Enrique David; Martín, David; García, Jesús; de la Escalera, Arturo; Molina, José Manuel; Armingol, José María

    2012-12-06

     The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.

  19. Fusion of Radar and EO-sensors for Surveillance

    NARCIS (Netherlands)

    Kester, L.J.H.M.; Theil, A.

    2000-01-01

    Fusion of radar and EO-sensors for the purpose of surveillance is investigated. All sensors are considered to be co-located with respect to the distance of the area under surveillance. More specifically, the applicability for such multi-sensor systems is examined for surveillance in littoral

  20. Fusion of Radar and EO-sensors for Surveillance

    NARCIS (Netherlands)

    Kester, L.J.H.M.; Theil, A.

    2001-01-01

    Fusion of radar and EO-sensors is investigated for the purpose of surveillance in littoral waters is. All sensors are considered to be co-located with respect to the distance, typically 1 to 10 km, of the area under surveillance. The sensor suite is a coherent polarimetric radar in combination with

  1. Fusion of Radar and EO-sensors for Surveillance

    NARCIS (Netherlands)

    Kester, L.J.H.M.; Theil, A.

    2001-01-01

    Fusion of radar and EO-sensors is investigated for the purpose of surveillance in littoral waters is. All sensors are considered to be co-located with respect to the distance, typically 1 to 10 km, of the area under surveillance. The sensor suite is a coherent polarimetric radar in combination with

  2. Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning

    Institute of Scientific and Technical Information of China (English)

    Zhang Yinan; Sun Qingwei; Quan He; Jin Yonggao; Quan Taifan

    2006-01-01

    In practical multi-sensor information fusion systems,there exists uncertainty about the network structure,active state of sensors,and information itself (including fuzziness,randomness,incompleteness as well as roughness,etc). Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.

  3. Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks

    CERN Document Server

    Bartolini, Novella; la Porta, Thomas; Petrioli, Chiara; Silvestri, Simone

    2010-01-01

    In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guarante...

  4. Multiple sensor fusion under unknown distributions

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.

    1996-10-01

    In a system of N sensors, the sensor {ital S{sub i}}, i = 1, 2 ..., N, outputs {ital Y}{sup (i)} {element_of} {Re}, according to an unknown probability distribution P{sub Y{sup (i)}}{vert_bar}X, corresponding to input X {element_of} {Re}. A training {ital n}-sample (X{sub 1}, Y{sub 1}), (X{sub 2},Y{sub 2}), ..., (X{sub n},Y{sub n}) is given where {ital Y{sub i}} = (Y{sub i}{sup (1)},Y{sub i}{sup (2)},...,Y{sub i}{sup (N)}) such that Y{sub i}{sup (j)} is the output of S{sub j} in response to input X{sub i}. The problem is to design a fusion rule expected square error: I({ital f}) = {integral}[X - f (Y)]{sup 2}dP{sub y{vert_bar}X}dPx, where Y=(Y{sup (1)}, Y{sup (2)},..., Y({sup N)}),is minimized over a family of functions {ital F}. Let f{sup *} minimize I(.) over {ital F}; in general, f{sup *} cannot be computed since the underlying distributions are unknown. We consider sufficient conditions based on smoothness and/or combinatorial dimensions of {ital F} to ensure that an estimator {cflx {ital f}} satisfies P[I({cflx {ital f}}) - I(f{sup *}) > {epsilon}] < {delta} for any {epsilon} > 0 and 0 < {delta} < 1. We present two methods for computing {cflx {ital f}} based on feedforward sigmoidal networks and Nadaraya-Watson estimator. Design and performance characteristics of the two methods are discussed, based both on theoretical and simulation results.

  5. Desensitized Optimal Filtering and Sensor Fusion Tool Kit Project

    Data.gov (United States)

    National Aeronautics and Space Administration — It is proposed to develop desensitized optimal filtering techniques and to implement these algorithms in a navigation and sensor fusion tool kit. These proposed...

  6. Distributed Fusion in Sensor Networks with Information Genealogy

    Science.gov (United States)

    2011-06-28

    1 llh International Conference on Information fusion. [2] KC Chang, CY Chong , and Shozo Mori, "On Scalable Distributed Sensor fusion," in Proc. 11...2011. [8] KC Chang, Chee-Yee Chong , and Shozo Mori, "Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms," IEEE Trans...Zhejiang University, Hang/. hou , China. He received the M.S. and Ph.D. in operations research from George Mason University, Fairfax, VA, in 2003 and

  7. Local adaptation and the evolution of chromosome fusions.

    Science.gov (United States)

    Guerrero, Rafael F; Kirkpatrick, Mark

    2014-10-01

    We use forward and coalescent models of population genetics to study chromosome fusions that reduce the recombination between two locally adapted loci. Under a continent-island model, a fusion spreads and reaches a polymorphic equilibrium when it causes recombination between locally adapted alleles to be less than their selective advantage. In contrast, fusions in a two-deme model always spread; whether it reaches a polymorphic equilibrium or becomes fixed depends on the relative recombination rates of fused homozygotes and heterozygotes. Neutral divergence around fusion polymorphisms is markedly increased, showing peaks at the point of fusion and at the locally adapted loci. Local adaptation could explain the evolution of many of chromosome fusions, which are some of the most common chromosome rearrangements in nature.

  8. Application of a sensor fusion algorithm for improving grasping stability

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae Hyeon; Yoon, Hyun Suck; Moon, Hyung Pil; Choi, Hyouk Ryeol; Koo Ja Choon [Sungkyunkwan University, Suwon (Korea, Republic of)

    2015-07-15

    A robot hand normally employees various sensors that are packaged in small form factor, perform with delicately accurate, and cost mostly very expensive. Grasping operation of the hand relies especially on accuracy of those sensors. Even with a set of advanced sensory systems embedded in a robot hand, securing a stable grasping is still challenging task. The present work makes an attempt to improve force sensor accuracy by applying sensor fusion method. An optimal weight value sensor fusion method formulated with Kalman filters is presented and tested in the work. Using a set of inexpensive sensors, the work achieves a reliable force sensing and applies the enhanced sensor stability to an object pinch grasping.

  9. Multi-sensor image fusion and its applications

    CERN Document Server

    Blum, Rick S

    2005-01-01

    Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies.After a review of state-of-the-art image fusion techniques,

  10. Sensor fusion for antipersonnel landmine detection, a case study

    NARCIS (Netherlands)

    Breejen, E. den; Schutte, K.; Cremer, F.

    1999-01-01

    In this paper the multi sensor fusion results obtained within the European research project GEODE (Ground Explosive Ordnance Detection system) are presented. The lay out of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves,

  11. Sensor fusion for antipersonnel landmine detection, a case study

    NARCIS (Netherlands)

    Breejen, E. den; Schutte, K.; Cremer, F.

    1999-01-01

    In this paper the multi sensor fusion results obtained within the European research project GEODE (Ground Explosive Ordnance Detection system) are presented. The lay out of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves,

  12. Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter.

    Science.gov (United States)

    Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

    2016-11-02

    Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system's error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the

  13. Dynamic gesture recognition based on multiple sensors fusion technology.

    Science.gov (United States)

    Wenhui, Wang; Xiang, Chen; Kongqiao, Wang; Xu, Zhang; Jihai, Yang

    2009-01-01

    This paper investigates the roles of a three-axis accelerometer, surface electromyography sensors and a webcam for dynamic gesture recognition. A decision-level multiple sensor fusion method based on action elements is proposed to distinguish a set of 20 kinds of dynamic hand gestures. Experiments are designed and conducted to collect three kinds of sensor data stream simultaneously during gesture implementation and compare the performance of different subsets in gesture recognition. Experimental results from three subjects show that the combination of three kinds of sensor achieves recognition accuracies at 87.5%-91.8%, which are higher largely than that of the single sensor conditions. This study is valuable to realize continuous and dynamic gesture recognition based on multiple sensor fusion technology for multi-model interaction.

  14. Force and Acceleration Sensor Fusion for Compliant Robot Motion Control

    OpenAIRE

    Gámez García, Javier; Robertsson, Anders; Gómez Ortega, Juan; Johansson, Rolf

    2005-01-01

    In this work, we present implementation and experiment of the theory of dynamic force sensing for robotic manipulators, which uses a sensor fusion technique in order to extract the contact force exerted by the end-effector of the manipulator from those measured by a wrist force sensor, which are corrupted by the inertial forces on the end-effector. We propose a new control strategy based on multisensor fusion with three different sensors that is, encoders mounted at each joint of the robot wi...

  15. ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

    Institute of Scientific and Technical Information of China (English)

    SONG Kaichen; NIE Xili

    2006-01-01

    Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion,are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

  16. Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

    Directory of Open Access Journals (Sweden)

    Marwah Almasri

    2015-12-01

    Full Text Available Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

  17. Sensor Fusion Based Model for Collision Free Mobile Robot Navigation.

    Science.gov (United States)

    Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

    2015-12-26

    Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and 24 fuzzy rules for the robot's movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

  18. Hierarchical adaptation scheme for multiagent data fusion and resource management in situation analysis

    Science.gov (United States)

    Benaskeur, Abder R.; Roy, Jean

    2001-08-01

    Sensor Management (SM) has to do with how to best manage, coordinate and organize the use of sensing resources in a manner that synergistically improves the process of data fusion. Based on the contextual information, SM develops options for collecting further information, allocates and directs the sensors towards the achievement of the mission goals and/or tunes the parameters for the realtime improvement of the effectiveness of the sensing process. Conscious of the important role that SM has to play in modern data fusion systems, we are currently studying advanced SM Concepts that would help increase the survivability of the current Halifax and Iroquois Class ships, as well as their possible future upgrades. For this purpose, a hierarchical scheme has been proposed for data fusion and resource management adaptation, based on the control theory and within the process refinement paradigm of the JDL data fusion model, and taking into account the multi-agent model put forward by the SASS Group for the situation analysis process. The novelty of this work lies in the unified framework that has been defined for tackling the adaptation of both the fusion process and the sensor/weapon management.

  19. 航迹融合算法在多传感器融合中的应用%Application of the Track Fusion in Multi-sensor Fusion

    Institute of Scientific and Technical Information of China (English)

    田雪怡; 李一兵; 李志刚

    2012-01-01

    The track fusion is an important aspect in the multi-sensor data fusion. Because of the public noise, the track estimate errors from the different sensors are not independent in the state estimate fusion system. So the fu sion problem becomes complex. This article researched the simple fusion, adaptive track fusion and weighted covari ance fusion. The comparison of data fusion methods shows that adaptive track fusion and weighted covariance fusion is effective to multi-sensor data fusion. The simulation indicates that the algorithm has preferable fusion result.%研究寻的制导优化控制问题,针对传统单一传感器导引不能满足性能要求,提出采用多传感器复合制导.航迹融合是多传感器数据融合中一个非常重要的方面.由于公共过程噪声的原因,使在应用状态估计融合系统中,来自不同传感器的航迹估计误差未必有独立性,为了使航迹与航迹关联和融合,提出自适应航迹和协方差加权航迹融合的算法.通过仿真研究说明自适应航迹融合和协方差加权航迹融合的算法对多传感器数据融合技术有很明显的作用,数据融合效果好,为复合寻的制导优化设计提供了依据.

  20. An Adaptive Altitude Information Fusion Method for Autonomous Landing Processes of Small Unmanned Aerial Rotorcraft

    Directory of Open Access Journals (Sweden)

    Xusheng Lei

    2012-09-01

    Full Text Available This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests.

  1. Sensor fusion methods for high performance active vibration isolation systems

    Science.gov (United States)

    Collette, C.; Matichard, F.

    2015-04-01

    Sensor noise often limits the performance of active vibration isolation systems. Inertial sensors used in such systems can be selected through a wide variety of instrument noise and size characteristics. However, the most sensitive instruments are often the biggest and the heaviest. Consequently, high-performance active isolators sometimes embed many tens of kilograms in instrumentation. The weight and size of instrumentation can add unwanted constraint on the design. It tends to lower the structures natural frequencies and reduces the collocation between sensors and actuators. Both effects tend to reduce feedback control performance and stability. This paper discusses sensor fusion techniques that can be used in order to increase the control bandwidth (and/or the stability). For this, the low noise inertial instrument signal dominates the fusion at low frequency to provide vibration isolation. Other types of sensors (relative motion, smaller but noisier inertial, or force sensors) are used at higher frequencies to increase stability. Several sensor fusion configurations are studied. The paper shows the improvement that can be expected for several case studies including a rigid equipment, a flexible equipment, and a flexible equipment mounted on a flexible support structure.

  2. Sensor Fusion-based Event Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, N.; Havinga, P.J.M.

    2009-01-01

    Recently, Wireless Sensor Networks (WSN) community has witnessed an application focus shift. Although, monitoring was the initial application of wireless sensor networks, in-network data processing and (near) real-time actuation capability have made wireless sensor networks suitable candidate for ev

  3. Probabilistic Tracking of Objects with Adaptive Cue Fusion Mechanism

    Institute of Scientific and Technical Information of China (English)

    ZHANG Bo; TIAN Wei-feng; JIN Zhi-hua

    2007-01-01

    A tracking method based on adaptive multiple cue fusion mechanism was presented, where particle filter is used to integrate color and edge cues. The fusion mechanism assigns different weights to two cues according to their importance, thus improving the robustness and reliability of the tracking algorithm. Moreover, a multi-part color model is also invoked to deal with the confliction among similar objects. The experimental results on two real image sequences show the tracking algorithm with adaptive fusion mechanism performs well in the presence of complex scenarios such as head rotation, scale change and multiple person occlusions.

  4. Data Fusion in Distributed Multi-sensor System

    Institute of Scientific and Technical Information of China (English)

    GUO Hang; YU Min

    2004-01-01

    This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors' data processing. First, a residual χ2-test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real-time data. A pseudolite (PL) simulation example is given.

  5. Context extraction for local fusion for landmine detection with multi-sensor systems

    Science.gov (United States)

    Frigui, Hichem; Gader, Paul D.; Ben Abdallah, Ahmed Chamseddine

    2009-05-01

    We present a local method for fusing the results of several landmine detectors using Ground Penetrating Radar (GPR) and Wideband Electro-Magnetic Induction (WEMI) sensors. The detectors considered include Edge Histogram Descriptor (EHD), Hidden Markov Models (HMM), and Spectral Correlation Feature (SCF) for the GPR sensor, and a feature-based classifier for the metal detector. The above detectors use different types of features and different classification methods. Our approach, called Context Extraction for Local Fusion with Feature Discrimination(CELF-FD), is a local approach that adapts the fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into clusters and identify the relevant features within each cluster. The fusion component thrives to learns the optimal fusion parameters within each cluster. Results on large and diverse GPR and WEMI data collections show that the proposed method can identify meaningful and coherent clusters and that these clusters require different fusion parameters. Our initial experiments have also indicated that CELF-FD outperforms the original CELF algorithm and all individual detectors.

  6. Fuzzy-Based Sensor Fusion for Cognitive Radio-Based Vehicular Ad Hoc and Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Jalil Piran

    2015-01-01

    Full Text Available In wireless sensor networks, sensor fusion is employed to integrate the acquired data from diverse sensors to provide a unified interpretation. The best and most salient advantage of sensor fusion is to obtain high-level information in both statistical and definitive aspects, which cannot be attained by a single sensor. In this paper, we propose a novel sensor fusion technique based on fuzzy theory for our earlier proposed Cognitive Radio-based Vehicular Ad Hoc and Sensor Networks (CR-VASNET. In the proposed technique, we considered four input sensor readings (antecedents and one output (consequent. The employed mobile nodes in CR-VASNET are supposed to be equipped with diverse sensors, which cater to our antecedent variables, for example, The Jerk, Collision Intensity, and Temperature and Inclination Degree. Crash_Severity is considered as the consequent variable. The processing and fusion of the diverse sensory signals are carried out by fuzzy logic scenario. Accuracy and reliability of the proposed protocol, demonstrated by the simulation results, introduce it as an applicable system to be employed to reduce the causalities rate of the vehicles’ crashes.

  7. Oil exploration oriented multi-sensor image fusion algorithm

    Science.gov (United States)

    Xiaobing, Zhang; Wei, Zhou; Mengfei, Song

    2017-04-01

    In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multi-sensor image fusion, especially for images of oil exploration.

  8. Multiple image sensor data fusion through artificial neural networks

    Science.gov (United States)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  9. Sensor Fusion and Model Verification for a Mobile Robot

    DEFF Research Database (Denmark)

    Bisgaard, Morten; Vinther, Dennis; Østergaard, Kasper Zinck

    2005-01-01

    This paper presents the results of modeling, sensor fusion and model verification for a four-wheel driven, four-wheel steered mobile robot moving in outdoor terrain. The model derived for the robot describes the actuator and wheel dynamics and the vehicle kinematics, and includes friction terms...

  10. Fault-tolerant Sensor Fusion for Marine Navigation

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2006-01-01

    where essential navigation information is provided even with multiple faults in instrumentation. The paper proposes a provable correct implementation through auto-generated state-event logics in a supervisory part of the algorithms. Test results from naval vessels document the performance and shows...... events where the fault-tolerant sensor fusion provided uninterrupted navigation data despite temporal instrument defects...

  11. Fault-tolerant Sensor Fusion for Marine Navigation

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2006-01-01

    where essential navigation information is provided even with multiple faults in instrumentation. The paper proposes a provable correct implementation through auto-generated state-event logics in a supervisory part of the algorithms. Test results from naval vessels document the performance and shows...... events where the fault-tolerant sensor fusion provided uninterrupted navigation data despite temporal instrument defects...

  12. Sensor fusion for active vibration isolation in precision equipment

    NARCIS (Netherlands)

    Tjepkema, D.; Dijk, van J.; Soemers, H.M.J.R.

    2012-01-01

    Sensor fusion is a promising control strategy to improve the performance of active vibration isolation systems that are used in precision equipment. Normally, those vibration isolation systems are only capable of realizing a low transmissibility. Additional objectives are to increase the damping rat

  13. Integration of multiple sensor fusion in controller design.

    Science.gov (United States)

    Abdelrahman, Mohamed; Kandasamy, Parameshwaran

    2003-04-01

    The main focus of this research is to reduce the risk of a catastrophic response of a feedback control system when some of the feedback data from the system sensors are not reliable, while maintaining a reasonable performance of the control system. In this paper a methodology for integrating multiple sensor fusion into the controller design is presented. The multiple sensor fusion algorithm produces, in addition to the estimate of the measurand, a parameter that measures the confidence in the estimated value. This confidence is integrated as a parameter into the controller to produce fast system response when the confidence in the estimate is high, and a slow response when the confidence in the estimate is low. Conditions for the stability of the system with the developed controller are discussed. This methodology is demonstrated on a cupola furnace model. The simulations illustrate the advantages of the new methodology.

  14. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks

    CERN Document Server

    Abdelgawad, Ahmed

    2012-01-01

    This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences.  These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative.   Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

  15. Sensor Fusion for Nuclear Proliferation Activity Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Adel Ghanem, Ph D

    2007-03-30

    The objective of Phase 1 of this STTR project is to demonstrate a Proof-of-Concept (PoC) of the Geo-Rad system that integrates a location-aware SmartTag (made by ZonTrak) and a radiation detector (developed by LLNL). It also includes the ability to transmit the collected radiation data and location information to the ZonTrak server (ZonService). The collected data is further transmitted to a central server at LLNL (the Fusion Server) to be processed in conjunction with overhead imagery to generate location estimates of nuclear proliferation and radiation sources.

  16. Multi-sensor image fusion using discrete wavelet frame transform

    Institute of Scientific and Technical Information of China (English)

    Zhenhua Li(李振华); Zhongliang Jing(敬忠良); Shaoyuan Sun(孙韶媛)

    2004-01-01

    An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT).The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.

  17. Sensor-fusion-based biometric identity verification

    Energy Technology Data Exchange (ETDEWEB)

    Carlson, J.J.; Bouchard, A.M.; Osbourn, G.C.; Martinez, R.F.; Bartholomew, J.W. [Sandia National Labs., Albuquerque, NM (United States); Jordan, J.B.; Flachs, G.M.; Bao, Z.; Zhu, L. [New Mexico State Univ., Las Cruces, NM (United States). Electronic Vision Research Lab.

    1998-02-01

    Future generation automated human biometric identification and verification will require multiple features/sensors together with internal and external information sources to achieve high performance, accuracy, and reliability in uncontrolled environments. The primary objective of the proposed research is to develop a theoretical and practical basis for identifying and verifying people using standoff biometric features that can be obtained with minimal inconvenience during the verification process. The basic problem involves selecting sensors and discovering features that provide sufficient information to reliably verify a person`s identity under the uncertainties caused by measurement errors and tactics of uncooperative subjects. A system was developed for discovering hand, face, ear, and voice features and fusing them to verify the identity of people. The system obtains its robustness and reliability by fusing many coarse and easily measured features into a near minimal probability of error decision algorithm.

  18. Fusion of Onboard Sensors for Better Navigation

    Directory of Open Access Journals (Sweden)

    Ravi Shankar

    2013-03-01

    Full Text Available This paper presents simulation results of navigation sensors such as integrated navigation system (INS, global navigation satellite system (GNSS and TACAN sensors onboard an aircraft to find the navigation solutions. Mathematical models for INS, GNSS (GPS satellite trajectories, GPS receiver and TACAN characteristics are simulated in Matlab. The INS simulation generates the output for position, velocity and attitude based on aerosond dynamic model. The GPS constellation is generated based on the YUMA almanac data. The GPS dilution of precession (DOP parameters are calculated and the best combination of four satellites (minimum PDOP is used for calculating the user position and velocity. The INS, GNSS, and TACAN solutions are integrated through loosely coupled extended Kalman filter for calculating the optimum navigation solution. The work is starting stone for providing aircraft based augmentation system for required navigation performance in terms of availability, accuracy, continuity and integrity.

  19. Multispectral Image Enhancement Through Adaptive Wavelet Fusion

    Science.gov (United States)

    2017-02-08

    Filtering. PeerJ Computer Science, 2, e72. doi: 10.7717/peerj-cs.72. https://peerj.com/articles/cs-72/ 6 Coloring multiband night vision images...decompose the source images into base and detail layers at multiple levels of resolution. Then, frequency-tuned filtering is used to compute saliency...obtains state-of-the-art performance for the fusion of multispectral night vision images. The method has a simple implementation and is computationally

  20. Data fusion of multiple kinect sensors for a rehabilitation system.

    Science.gov (United States)

    Huibin Du; Yiwen Zhao; Jianda Han; Zheng Wang; Guoli Song

    2016-08-01

    Kinect-like depth sensors have been widely used in rehabilitation systems. However, single depth sensor processes limb-blocking, data loss or data error poorly, making it less reliable. This paper focus on using two Kinect sensors and data fusion method to solve these problems. First, two Kinect sensors capture the motion data of the healthy arm of the hemiplegic patient; Second, merge the data using the method of Set-Membership-Filter (SMF); Then, mirror this motion data by the Middle-Plane; In the end, control the wearable robotic arm driving the patient's paralytic arm so that the patient can interactively and initiatively complete a variety of recovery actions prompted by computer with 3D animation games.

  1. Non-Linear Fusion of Observations Provided by Two Sensors

    Directory of Open Access Journals (Sweden)

    Monir Azmani

    2013-07-01

    Full Text Available When we try to make the best estimate of some quantity, the problem of combining results from different experiments is encountered. In multi-sensor data fusion, the problem is seen as combining observations provided by different sensors. Sensors provide observations and information on an unknown quantity, which can differ in precision. We propose a combined estimate that uses prior information. We consider the simpler aspects of the problem, so that two sensors provide an observation of the same quantity. The standard error of the observations is supposed to be known. The prior information is an interval that bounds the parameter of the estimate. We derive the proposed combined estimate methodology, and we show its efficiency in the minimum mean square sense. The proposed combined estimate is assessed using synthetic data, and an application is presented.

  2. Fusion of Onboard Sensors for Better Navigation

    Directory of Open Access Journals (Sweden)

    Ravi Shankar

    2013-03-01

    Full Text Available This paper presents simulation results of navigation sensors such as integrated navigation system (INS, global navigation satellite system (GNSS and TACAN sensors onboard an aircraft to find the navigation solutions. Mathematical models for INS, GNSS (GPS satellite trajectories, GPS receiver and TACAN characteristics are simulated in Matlab. The INS simulation generates the output for position, velocity and attitude based on aerosond dynamic model. The GPS constellation is generated based on the YUMA almanac data. The GPS dilution of precession (DOP parameters are calculated and the best combination of four satellites (minimum PDOP is used for calculating the user position and velocity. The INS, GNSS, and TACAN solutions are integrated through loosely coupled extended Kalman filter for calculating the optimum navigation solution. The work is starting stone for providing aircraft based augmentation system for required navigation performance in terms of availability, accuracy, continuity and integrity.Defence Science Journal, 2013, 63(2, pp.145-152, DOI:http://dx.doi.org/10.14429/dsj.63.4256

  3. Distributed Sensor Fusion for Scalar Field Mapping Using Mobile Sensor Networks.

    Science.gov (United States)

    La, Hung Manh; Sheng, Weihua

    2013-04-01

    In this paper, autonomous mobile sensor networks are deployed to measure a scalar field and build its map. We develop a novel method for multiple mobile sensor nodes to build this map using noisy sensor measurements. Our method consists of two parts. First, we develop a distributed sensor fusion algorithm by integrating two different distributed consensus filters to achieve cooperative sensing among sensor nodes. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed, which allows each sensor node to find an estimate of the value of the scalar field at each time step. In the second phase, the average consensus filter is used to allow each sensor node to find a confidence of the estimate at each time step. The final estimate of the value of the scalar field is iteratively updated during the movement of the mobile sensors via weighted average. Second, we develop the distributed flocking-control algorithm to drive the mobile sensors to form a network and track the virtual leader moving along the field when only a small subset of the mobile sensors know the information of the leader. Experimental results are provided to demonstrate our proposed algorithms.

  4. Diagnostics and data fusion of robotic sensors

    Energy Technology Data Exchange (ETDEWEB)

    Dhar, M.; Bardsley, S; Cowper, L.; Hamm, R.; Jammu, V.; Wagner, J.

    1996-12-31

    Robotic systems for remediation of hazardous waste sites must be highly reliable to avoid equipment failures and subsequent possible exposure of personnel to hazardous environments. Safe, efficient cleanup operations also require accurate, complete knowledge of the task space. This paper presents progress made on a 18 month program to meet these needs. To enhance robot reliability, a conceptual design of a monitoring and diagnostic system is being developed to predict the onset of mechanical failure modes, provide maximum lead time to make operational changes or repairs, and minimize the occurrence of on-site breakdowns. To ensure safe operation, a comprehensive software package is being developed that will fuse data from multiple surface mapping sensors and poses so as to reduce the error effects in individual data points and provide accurate 3-D maps of a work space.

  5. Sensor fusion by pseudo information measure: a mobile robot application.

    Science.gov (United States)

    Asharif, Mohammad Reza; Moshiri, Behzad; HoseinNezhad, Reza

    2002-07-01

    In any autonomous mobile robot, one of the most important issues to be designed and implemented is environment perception. In this paper, a new approach is formulated in order to perform sensory data integration for generation of an occupancy grid map of the environment. This method is an extended version of the Bayesian fusion method for independent sources of information. The performance of the proposed method of fusion and its sensitivity are discussed. Map building simulation for a cylindrical robot with eight ultrasonic sensors and mapping implementation for a Khepera robot have been separately tried in simulation and experimental works. A new neural structure is introduced for conversion of proximity data that are given by Khepera IR sensors to occupancy probabilities. Path planning experiments have also been applied to the resulting maps. For each map, two factors are considered and calculated: the fitness and the augmented occupancy of the map with respect to the ideal map. The length and the least distance to obstacles were the other two factors that were calculated for the routes that are resulted by path planning experiments. Experimental and simulation results show that by using the new fusion formulas, more informative maps of the environment are obtained. By these maps more appropriate routes could be achieved. Actually, there is a tradeoff between the length of the resulting routes and their safety and by choosing the proper fusion function, this tradeoff is suitably tuned for different map building applications.

  6. Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

    Directory of Open Access Journals (Sweden)

    Schutte Klamer

    2006-01-01

    Full Text Available We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC, in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.

  7. Towards an operational sensor-fusion system for anti-personnel landmine detection

    NARCIS (Netherlands)

    Cremer, F.; Schutte, K.; Schavemaker, J.G.M.; Breejen, E. den

    2000-01-01

    To acquire detection performance required for an operational system for the detection of anti-personnel landmines, it is necessary to use multiple sensors and sensor-fusion techniques. This paper describes five decision-level sensor-fusion techniques and their common optimisation method. The perform

  8. Cognitive foundations for model-based sensor fusion

    Science.gov (United States)

    Perlovsky, Leonid I.; Weijers, Bertus; Mutz, Chris W.

    2003-08-01

    Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating "biological lessons" into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with

  9. Design of Real-time Communication Adapter for Different Protocol Sensors in Sensor Web

    Directory of Open Access Journals (Sweden)

    Longlong Lu

    2012-09-01

    Full Text Available A real-time communication adapter named SensorAdapter is designed to communicate between different protocols sensors and data service layer in Sensor Web. The adapter is extended and restructured based on SensorBus, an open source project raised by a German company called 52north. By structuring the receiving module and extending the proxies of sensors according to the communication protocols the sensors use, the adapter can receive sensing information detected by different protocols sensors simultaneously. The receiving module identifies a sensor and finds its corresponding proxy in SensorAdapter by sensor ID (SensorID, and then packages the sensing information to XMPP messages and sends them to XMPPServer by invoking the methods in its proxy. At last, an example of SOS is achieved to verify the effect of the adapter.

  10. Adaptive image fusion based on nonsubsampled contourlet transform

    Science.gov (United States)

    Zhang, Xiongmei; Li, Junshan; Yi, Zhaoxiang; Yang, Wei

    2007-11-01

    Multiresolution-based image fusion has been the focus of considerable research attention in recent years with a number of algorithms proposed. In most of the algorithms, however, the parameter configuration is usually based on experience. This paper proposes an adaptive image fusion algorithm based on the nonsubsampled contourlet transform (NSCT), which realizes automatic parameter adjustment and gets rid of the adverse effect caused by artificial factors. The algorithm incorporates the quality metric of structural similarity (SSIM) into the NSCT fusion framework. The SSIM value is calculated to assess the fused image quality, and then it is fed back to the fusion algorithm to achieve a better fusion by directing parameters (level of decomposition and flag of decomposition direction) adjustment. Based on the cross entropy, the local cross entropy (LCE) is constructed and used to determine an optimal choice of information source for the fused coefficients at each scale and direction. Experimental results show that the proposed method achieves the best fusion compared to three other methods judged on both the objective metrics and visual inspection and exhibits robust against varying noises.

  11. Contribution of sensor fusion to urban mapping: application to simulated SPOT 5-6 data

    OpenAIRE

    1996-01-01

    International audience; This communication intends to enhance the contribution of a sensor fusion method to urban mapping using the simulated SPOT 5-6 data. A new scheme is proposed for cartography of urban areas which takes into account the multispectral and the multiresolution nature of the data. This process makes use of classification and segmentation. An application of the sensor fusion method to analyse the simulated SPOT 5-6 data is presented. The benefits of using sensor fusion before...

  12. AN INFORMATION FUSION METHOD FOR SENSOR DATA RECTIFICATION

    Institute of Scientific and Technical Information of China (English)

    Zhang Zhen; Xu Lizhong; Harry Hua Li; Shi Aiye; Han Hua; Wang Huibin

    2012-01-01

    In the applications of water regime monitoring,incompleteness,and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information.Based on the spatial and temporal correlation of water regime monitoring information,this paper addresses this issue and proposes an information fusion method to implement data rectification.An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit,which takes Field-Programmable Gate Array (FPGA) as the core component.In order to verify the effectiveness,five measurements including water level,discharge and velocity are selected from three different points in a water regime monitoring station.The simulation results show that this method can recitify random errors as well as gross errors significantly.

  13. Discrete Kalman Filter based Sensor Fusion for Robust Accessibility Interfaces

    Science.gov (United States)

    Ghersi, I.; Mariño, M.; Miralles, M. T.

    2016-04-01

    Human-machine interfaces have evolved, benefiting from the growing access to devices with superior, embedded signal-processing capabilities, as well as through new sensors that allow the estimation of movements and gestures, resulting in increasingly intuitive interfaces. In this context, sensor fusion for the estimation of the spatial orientation of body segments allows to achieve more robust solutions, overcoming specific disadvantages derived from the use of isolated sensors, such as the sensitivity of magnetic-field sensors to external influences, when used in uncontrolled environments. In this work, a method for the combination of image-processing data and angular-velocity registers from a 3D MEMS gyroscope, through a Discrete-time Kalman Filter, is proposed and deployed as an alternate user interface for mobile devices, in which an on-screen pointer is controlled with head movements. Results concerning general performance of the method are presented, as well as a comparative analysis, under a dedicated test application, with results from a previous version of this system, in which the relative-orientation information was acquired directly from MEMS sensors (3D magnetometer-accelerometer). These results show an improved response for this new version of the pointer, both in terms of precision and response time, while keeping many of the benefits that were highlighted for its predecessor, giving place to a complementary method for signal acquisition that can be used as an alternative-input device, as well as for accessibility solutions.

  14. Adaptive Fusion of Stochastic Information for Imaging Fractured Vadose Zones

    Science.gov (United States)

    Daniels, J.; Yeh, J.; Illman, W.; Harri, S.; Kruger, A.; Parashar, M.

    2004-12-01

    A stochastic information fusion methodology is developed to assimilate electrical resistivity tomography, high-frequency ground penetrating radar, mid-range-frequency radar, pneumatic/gas tracer tomography, and hydraulic/tracer tomography to image fractures, characterize hydrogeophysical properties, and monitor natural processes in the vadose zone. The information technology research will develop: 1) mechanisms and algorithms for fusion of large data volumes ; 2) parallel adaptive computational engines supporting parallel adaptive algorithms and multi-physics/multi-model computations; 3) adaptive runtime mechanisms for proactive and reactive runtime adaptation and optimization of geophysical and hydrological models of the subsurface; and 4) technologies and infrastructure for remote (pervasive) and collaborative access to computational capabilities for monitoring subsurface processes through interactive visualization tools. The combination of the stochastic fusion approach and information technology can lead to a new level of capability for both hydrologists and geophysicists enabling them to "see" into the earth at greater depths and resolutions than is possible today. Furthermore, the new computing strategies will make high resolution and large-scale hydrological and geophysical modeling feasible for the private sector, scientists, and engineers who are unable to access supercomputers, i.e., an effective paradigm for technology transfer.

  15. Sensor Fusion - Sonar and Stereo Vision, Using Occupancy Grids and SIFT

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Bendtsen, Jan Dimon

    2006-01-01

    The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a SDF (Sensor Data Fusion) architecture. This approach involves combined sonar and stereo vision readings. Sonar readings are interpreted using probability density functions to the o......The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a SDF (Sensor Data Fusion) architecture. This approach involves combined sonar and stereo vision readings. Sonar readings are interpreted using probability density functions...

  16. An alternative sensor fusion method for object orientation using low-cost MEMS inertial sensors

    Science.gov (United States)

    Bouffard, Joshua L.

    This thesis develops an alternative sensor fusion approach for object orientation using low-cost MEMS inertial sensors. The alternative approach focuses on the unique challenges of small UAVs. Such challenges include the vibrational induced noise onto the accelerometer and bias offset errors of the rate gyroscope. To overcome these challenges, a sensor fusion algorithm combines the measured data from the accelerometer and rate gyroscope to achieve a single output free from vibrational noise and bias offset errors. One of the most prevalent sensor fusion algorithms used for orientation estimation is the Extended Kalman filter (EKF). The EKF filter performs the fusion process by first creating the process model using the nonlinear equations of motion and then establishing a measurement model. With the process and measurement models established, the filter operates by propagating the mean and covariance of the states through time. The success of EKF relies on the ability to establish a representative process and measurement model of the system. In most applications, the EKF measurement model utilizes the accelerometer and GPS-derived accelerations to determine an estimate of the orientation. However, if the GPS-derived accelerations are not available then the measurement model becomes less reliable when subjected to harsh vibrational environments. This situation led to the alternative approach, which focuses on the correlation between the rate gyroscope and accelerometer-derived angle. The correlation between the two sensors then determines how much the algorithm will use one sensor over the other. The result is a measurement that does not suffer from the vibrational noise or from bias offset errors.

  17. OBSTACLE DETECTION SYSTEM INVOLVING FUSION OF MULTIPLE SENSOR TECHNOLOGIES

    Directory of Open Access Journals (Sweden)

    C. Giannì

    2017-08-01

    Full Text Available Obstacle detection is a fundamental task for Unmanned Aerial Vehicles (UAV as a part of a Sense and Avoid system. In this study, we present a method of multi-sensor obstacle detection that demonstrated good results on different kind of obstacles. This method can be implemented on low-cost platforms involving a DSP or small FPGA. In this paper, we also present a study on the typical targets that can be tough to detect because of their characteristics of reflectivity, form factor, heterogeneity and show how data fusion can often overcome the limitations of each technology.

  18. Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction

    Directory of Open Access Journals (Sweden)

    Shengli Zhou

    2014-08-01

    Full Text Available The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model.

  19. Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

    Directory of Open Access Journals (Sweden)

    Sondre Sanden Tordal

    2017-04-01

    Full Text Available This paper presents a novel approach for estimating the relative motion between two moving offshore vessels. The method is based on a sensor fusion algorithm including a vision system and two motion reference units (MRUs. The vision system makes use of the open-source computer vision library OpenCV and a cube with Aruco markers placed onto each of the cube sides. The Extended Quaternion Kalman Filter (EQKF is used for bad pose rejection for the vision system. The presented sensor fusion algorithm is based on the Indirect Feedforward Kalman Filter for error estimation. The system is self-calibrating in the sense that the Aruco cube can be placed in an arbitrary location on the secondary vessel. Experimental 6-DOF results demonstrate the accuracy and efficiency of the proposed sensor fusion method compared with the internal joint sensors of two Stewart platforms and the industrial robot. The standard deviation error was found to be 31mm or better when the Arcuo cube was placed at three different locations.

  20. Phase sensor for solar adaptive-optics

    CERN Document Server

    Kellerer, Aglae

    2011-01-01

    Wavefront sensing in solar adaptive-optics is currently done with correlating Shack-Hartmann sensors, although the spatial- and temporal-resolutions of the phase measurements are then limited by the extremely fast computing required to correlate the sensor signals at the frequencies of daytime atmospheric-fluctuations. To avoid this limitation, a new wavefront-sensing technique is presented, that makes use of the solar brightness and is applicable to extended sources. The wavefront is sent through a modified Mach-Zehnder interferometer. A small, central part of the wavefront is used as reference and is made to interfere with the rest of the wavefront. The contrast of two simultaneously measured interference-patterns provides a direct estimate of the wavefront phase, no additional computation being required. The proposed optical layout shows precise initial alignment to be the critical point in implementing the new wavefront-sensing scheme.

  1. Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle

    Directory of Open Access Journals (Sweden)

    Yibing Zhao

    2013-01-01

    Full Text Available Unmanned Ground Vehicles (UGVs that can drive autonomously in cross-country environment have received a good deal of attention in recent years. They must have the ability to determine whether the current terrain is traversable or not by using onboard sensors. This paper explores new methods related to environment perception based on computer image processing, pattern recognition, multisensors data fusion, and multidisciplinary theory. Kalman filter is used for low-level fusion of physical level, thus using the D-S evidence theory for high-level data fusion. Probability Test and Gaussian Mixture Model are proposed to obtain the traversable region in the forward-facing camera view for UGV. One feature set including color and texture information is extracted from areas of interest and combined with a classifier approach to resolve two types of terrain (traversable or not. Also, three-dimension data are employed; the feature set contains components such as distance contrast of three-dimension data, edge chain-code curvature of camera image, and covariance matrix based on the principal component method. This paper puts forward one new method that is suitable for distributing basic probability assignment (BPA, based on which D-S theory of evidence is employed to integrate sensors information and recognize the obstacle. The subordination obtained by using the fuzzy interpolation is applied to calculate the basic probability assignment. It is supposed that the subordination is equal to correlation coefficient in the formula. More accurate results of object identification are achieved by using the D-S theory of evidence. Control on motion behavior or autonomous navigation for UGV is based on the method, which is necessary for UGV high speed driving in cross-country environment. The experiment results have demonstrated the viability of the new method.

  2. Neuromorphic Audio-Visual Sensor Fusion on a Sound-Localising Robot

    Directory of Open Access Journals (Sweden)

    Vincent Yue-Sek Chan

    2012-02-01

    Full Text Available This paper presents the first robotic system featuring audio-visual sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localisation through self-motion and visual feedback, using an adaptive ITD-based sound localisation algorithm. After training, the robot can localise sound sources (white or pink noise in a reverberant environment with an RMS error of 4 to 5 degrees in azimuth. In the second part of the paper, we investigate the source binding problem. An experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. The results show that this technique can be quite effective, despite its simplicity.

  3. Semantically enriched data for effective sensor data fusion

    Science.gov (United States)

    de Mel, Geeth; Pham, Tien; Damarla, Thyagaraju; Vasconcelos, Wamberto; Norman, Tim

    2011-06-01

    Data fusion plays a major role in assisting decision makers by providing them with an improved situational awareness so that informed decisions could be made about the events that occur in the field. This involves combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete, dependable etc.) than what it would have been if the data streams (hereinafter referred to as 'feeds') from the resources are taken individually. However, these feeds lack any context-related information (e.g., detected event, event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources. In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL), OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.

  4. Data fusion for target tracking and classification with wireless sensor network

    Science.gov (United States)

    Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

    2016-10-01

    In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

  5. Sensor fusion III: 3-D perception and recognition; Proceedings of the Meeting, Boston, MA, Nov. 5-8, 1990

    Science.gov (United States)

    Schenker, Paul S. (Editor)

    1991-01-01

    The volume on data fusion from multiple sources discusses fusing multiple views, temporal analysis and 3D motion interpretation, sensor fusion and eye-to-hand coordination, and integration in human shape perception. Attention is given to surface reconstruction, statistical methods in sensor fusion, fusing sensor data with environmental knowledge, computational models for sensor fusion, and evaluation and selection of sensor fusion techniques. Topics addressed include the structure of a scene from two and three projections, optical flow techniques for moving target detection, tactical sensor-based exploration in a robotic environment, and the fusion of human and machine skills for remote robotic operations. Also discussed are K-nearest-neighbor concepts for sensor fusion, surface reconstruction with discontinuities, a sensor-knowledge-command fusion paradigm for man-machine systems, coordinating sensing and local navigation, and terrain map matching using multisensing techniques for applications to autonomous vehicle navigation.

  6. Wavefront sensors for adaptive optical systems

    Science.gov (United States)

    Lukin, V. P.; Botygina, N. N.; Emaleev, O. N.; Konyaev, P. A.

    2010-10-01

    A high precision Shack-Hartmann wavefront (WF) sensor has been developed on the basis of a low-aperture off-axis diffraction lens array. The device is capable of measuring WF slopes at array sub-apertures of size 640x640 μm with an error not exceeding 4.80 arcsec (0.15 pixel), which corresponds to the standard deviation equal to 0.017λ at the reconstructed WF with wavelength λ . Also the modification of this sensor for adaptive system of solar telescope using extended scenes as tracking objects, such as sunspot, pores, solar granulation and limb, is presented. The software package developed for the proposed WF sensors includes three algorithms of local WF slopes estimation (modified centroids, normalized cross-correlation and fast Fourier-demodulation), as well as three methods of WF reconstruction (modal Zernike polynomials expansion, deformable mirror response functions expansion and phase unwrapping), that can be selected during operation with accordance to the application.

  7. Optimal multi-sensor Kalman smoothing fusion for discrete multichannel ARMA signals

    Institute of Scientific and Technical Information of China (English)

    Shuli SUN

    2005-01-01

    Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense,using white noise estimators,an optimal fusion distributed Kalman smoother is given for discrete multi-channel ARMA (autoregressive moving average) signals.The smoothing error cross-covariance matrices between any two sensors are given for measurement noises.Furthermore,the fusion smoother gives higher precision than any local smoother does.

  8. Prospects of steady state magnetic diagnostic of fusion reactors based on metallic Hall sensors

    Science.gov (United States)

    Ďuran, I.; Sentkerestiová, J.; Kovařík, K.; Viererbl, L.

    2012-06-01

    Employment of sensors based on Hall effect (Hall sensors) is one of the candidate approaches to detection of almost steady state magnetic fields in future fusion reactors based on magnetic confinement (tokamaks, stellarators etc.), and also in possible fusion-fission hybrid systems having these fusion reactors as a neutron source and driver. This contribution reviews the initial considerations concerning application of metallic Hall sensors in fusion reactor harsh environment that include high neutron loads (>1018 cm-2) and elevated temperatures (>200°C). In particular, the candidate sensing materials, candidate technologies for sensors production, initial analysis of activation and transmutation of sensors under reactor relevant neutron loads and the tests of the the first samples of copper Hall sensors are presented.

  9. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2008-04-01

    Full Text Available Due to the wide potential applications of wireless sensor networks, this topic has attracted great attention. The strict energy constraints of sensor nodes result in great challenges for energy efficiency. This paper proposes an energy-efficient organization method. The organization of wireless sensor networks is formulated for target tracking. Target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Combining autoregressive moving average (ARMA model and radial basis function networks (RBFNs, robust target position forecasting is performed. Moreover, an energyefficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks are awakened in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of ARMA model and RBFN can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.

  10. Tele-Supervised Adaptive Ocean Sensor Fleet

    Science.gov (United States)

    Lefes, Alberto; Podnar, Gregg W.; Dolan, John M.; Hosler, Jeffrey C.; Ames, Troy J.

    2009-01-01

    The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate

  11. A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

    Directory of Open Access Journals (Sweden)

    Mohamed Bakillah

    2013-06-01

    Full Text Available Sensors play an increasingly critical role in capturing and distributing observation of phenomena in our environment. The Semantic Sensor Web enables interoperability to support various applications that use data made available by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping that adapts to dynamic metadata of sensors are required. Semantic mapping for Sensor Web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few applications. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial and temporal features that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings.

  12. Knowledge assistant: A sensor fusion framework for robotic environmental characterization

    Energy Technology Data Exchange (ETDEWEB)

    Feddema, J.T.; Rivera, J.J.; Tucker, S.D.

    1996-12-01

    A prototype sensor fusion framework called the {open_quotes}Knowledge Assistant{close_quotes} has been developed and tested on a gantry robot at Sandia National Laboratories. This Knowledge Assistant guides the robot operator during the planning, execution, and post analysis stages of the characterization process. During the planning stage, the Knowledge Assistant suggests robot paths and speeds based on knowledge of sensors available and their physical characteristics. During execution, the Knowledge Assistant coordinates the collection of data through a data acquisition {open_quotes}specialist.{close_quotes} During execution and post analysis, the Knowledge Assistant sends raw data to other {open_quotes}specialists,{close_quotes} which include statistical pattern recognition software, a neural network, and model-based search software. After the specialists return their results, the Knowledge Assistant consolidates the information and returns a report to the robot control system where the sensed objects and their attributes (e.g. estimated dimensions, weight, material composition, etc.) are displayed in the world model. This paper highlights the major components of this system.

  13. SENSOR FUSION CONTROL SYSTEM FOR COMPUTER INTEGRATED MANUFACTURING

    Directory of Open Access Journals (Sweden)

    C.M. Kumile

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: Manufacturing companies of today face unpredictable, high frequency market changes driven by global competition. To stay competitive, these companies must have the characteristics of cost-effective rapid response to the market needs. As an engineering discipline, mechatronics strives to integrate mechanical, electronic, and computer systems optimally in order to create high precision products and manufacturing processes. This paper presents a methodology of increasing flexibility and reusability of a generic computer integrated manufacturing (CIM cell-control system using simulation and modelling of mechatronic sensory system (MSS concepts. The utilisation of sensors within the CIM cell is highlighted specifically for data acquisition, analysis, and multi-sensor data fusion. Thus the designed reference architecture provides comprehensive insight for the functions and methodologies of a generic shop-floor control system (SFCS, which consequently enables the rapid deployment of a flexible system.

    AFRIKAANSE OPSOMMING: Hedendaagse vervaardigingsondernemings ervaar gereeld onvoorspelbare markveranderinge wat aangedryf word deur wêreldwye mededinging. Om kompeterend te bly moet hierdie ondernemings die eienskappe van kosteeffektiwiteit en snelle-respons op markfluktuasies toon. Megatronika streef daarna om meganiese, elektroniese en rekenaarstelsels optimaal te integreer om hoëpresisieprodukte en produksieprosesse daar te stel. Hierdie artikel suggereer 'n metodologie vir toenemende aanpasbaarheid en herbruikbaarheid van 'n generiese rekenaargeïntegreerde vervaardigingsel-beheersisteem deur die gebruik van simulasie en die modellering van megatroniese sensorsisteemkonsepte. Die aanwending van sensors binne die sel fasiliteer datavaslegging, ontleding en multisensordatafusie. Sodoende verskaf die ontwerpte argitektuur insig in die funksie en metodologie van 'n generiese stukwerkwinkelbeheersisteem wat die vinnige

  14. CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Crl7Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

  15. Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Wenyu; Zhang, Zhenjiang

    2015-08-19

    Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier's training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster's combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule.

  16. System Dynamics and Adaptive Control for MEMS Gyroscope Sensor

    OpenAIRE

    Juntao Fei; Hongfei Ding

    2010-01-01

    This paper presents an adaptive control approach for Micro-Electro-Mechanical Systems (MEMS) z-axis gyroscope sensor. The dynamical model of MEMS gyroscope sensor is derived and adaptive state tracking control for MEMS gyroscope is developed. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms due to the fabrication imperfection. The stability of the closed-loop systems is established with the propo...

  17. System Dynamics and Adaptive Control for MEMS Gyroscope Sensor

    OpenAIRE

    Juntao Fei; Hongfei Ding

    2011-01-01

    This paper presents an adaptive control approach for Micro-Electro-Mechanical Systems (MEMS) z-axis gyroscope sensor. The dynamical model of MEMS gyroscope sensor is derived and adaptive state tracking control for MEMS gyroscope is developed. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms due to the fabrication imperfection. The stability of the closed-loop systems is established with the propo...

  18. Adaptive Sensing Based on Profiles for Sensor Systems

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-10-01

    Full Text Available This paper proposes a profile-based sensing framework for adaptive sensor systems based on models that relate possibly heterogeneous sensor data and profiles generated by the models to detect events. With these concepts, three phases for building the sensor systems are extracted from two examples: a combustion control sensor system for an automobile engine, and a sensor system for home security. The three phases are: modeling, profiling, and managing trade-offs. Designing and building a sensor system involves mapping the signals to a model to achieve a given mission.

  19. Adaptive Sensor Activity Scheduling in Distributed Sensor Networks: A Statistical Mechanics Approach

    OpenAIRE

    Abhishek Srivastav; Asok Ray; Shashi Phoha

    2009-01-01

    This article presents an algorithm for adaptive sensor activity scheduling (A-SAS) in distributed sensor networks to enable detection and dynamic footprint tracking of spatial-temporal events. The sensor network is modeled as a Markov random field on a graph, where concepts of Statistical Mechanics are employed to stochastically activate the sensor nodes. Using an Ising-like formulation, the sleep and wake modes of a sensor node are modeled as spins with ferromagnetic neighborhood interaction...

  20. Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

    Science.gov (United States)

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-12-13

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  1. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liao

    2013-12-01

    Full Text Available Fault diagnosis (FD and data fusion (DF technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2 sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  2. Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks

    Directory of Open Access Journals (Sweden)

    Elena Bergamini

    2014-10-01

    Full Text Available Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter and complementary (Non-linear observer filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles and heading (yaw angle errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided.

  3. Soft sensor design by multivariate fusion of image features and process measurements

    DEFF Research Database (Denmark)

    Lin, Bao; Jørgensen, Sten Bay

    2011-01-01

    This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine...... oxides (NOx) emission of cement kilns. On-site tests demonstrate improved performance over soft sensors based on conventional process measurements only....

  4. Asynchronous Sensor fuSion for Improved Safety of air Traffic (ASSIST) Project

    Data.gov (United States)

    National Aeronautics and Space Administration — SSCI proposes to develop, implement and test a collision detection system for unmanned aerial vehicles (UAV), referred to as the Asynchronous Sensor fuSion for...

  5. Distributed data fusion across multiple hard and soft mobile sensor platforms

    Science.gov (United States)

    Sinsley, Gregory

    One of the biggest challenges currently facing the robotics field is sensor data fusion. Unmanned robots carry many sophisticated sensors including visual and infrared cameras, radar, laser range finders, chemical sensors, accelerometers, gyros, and global positioning systems. By effectively fusing the data from these sensors, a robot would be able to form a coherent view of its world that could then be used to facilitate both autonomous and intelligent operation. Another distinct fusion problem is that of fusing data from teammates with data from onboard sensors. If an entire team of vehicles has the same worldview they will be able to cooperate much more effectively. Sharing worldviews is made even more difficult if the teammates have different sensor types. The final fusion challenge the robotics field faces is that of fusing data gathered by robots with data gathered by human teammates (soft sensors). Humans sense the world completely differently from robots, which makes this problem particularly difficult. The advantage of fusing data from humans is that it makes more information available to the entire team, thus helping each agent to make the best possible decisions. This thesis presents a system for fusing data from multiple unmanned aerial vehicles, unmanned ground vehicles, and human observers. The first issue this thesis addresses is that of centralized data fusion. This is a foundational data fusion issue, which has been very well studied. Important issues in centralized fusion include data association, classification, tracking, and robotics problems. Because these problems are so well studied, this thesis does not make any major contributions in this area, but does review it for completeness. The chapter on centralized fusion concludes with an example unmanned aerial vehicle surveillance problem that demonstrates many of the traditional fusion methods. The second problem this thesis addresses is that of distributed data fusion. Distributed data fusion

  6. Multimodal Medical Image Fusion by Adaptive Manifold Filter

    Directory of Open Access Journals (Sweden)

    Peng Geng

    2015-01-01

    Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

  7. Dynamic reweighting of three modalities for sensor fusion.

    Directory of Open Access Journals (Sweden)

    Sungjae Hwang

    Full Text Available We simultaneously perturbed visual, vestibular and proprioceptive modalities to understand how sensory feedback is re-weighted so that overall feedback remains suited to stabilizing upright stance. Ten healthy young subjects received an 80 Hz vibratory stimulus to their bilateral Achilles tendons (stimulus turns on-off at 0.28 Hz, a ± 1 mA binaural monopolar galvanic vestibular stimulus at 0.36 Hz, and a visual stimulus at 0.2 Hz during standing. The visual stimulus was presented at different amplitudes (0.2, 0.8 deg rotation about ankle axis to measure: the change in gain (weighting to vision, an intramodal effect; and a change in gain to vibration and galvanic vestibular stimulation, both intermodal effects. The results showed a clear intramodal visual effect, indicating a de-emphasis on vision when the amplitude of visual stimulus increased. At the same time, an intermodal visual-proprioceptive reweighting effect was observed with the addition of vibration, which is thought to change proprioceptive inputs at the ankles, forcing the nervous system to rely more on vision and vestibular modalities. Similar intermodal effects for visual-vestibular reweighting were observed, suggesting that vestibular information is not a "fixed" reference, but is dynamically adjusted in the sensor fusion process. This is the first time, to our knowledge, that the interplay between the three primary modalities for postural control has been clearly delineated, illustrating a central process that fuses these modalities for accurate estimates of self-motion.

  8. Adaptive Genetic Algorithm for Sensor Coarse Signal Processing

    Directory of Open Access Journals (Sweden)

    Xuan Huang

    2014-03-01

    Full Text Available As with the development of computer technology and informatization, network technique, sensor technique and communication technology become three necessary components of information industry. As the core technique of sensor application, signal processing mainly determines the sensor performances. For this reason, study on signal processing mode is very important to sensors and the application of sensor network. In this paper, we introduce a new sensor coarse signal processing mode based on adaptive genetic algorithm. This algorithm selects crossover, mutation probability adaptively and compensates multiple operators commutatively to optimize the search process, so that we can obtain the global optimum solution. Based on the proposed algorithm, using auto-correlative characteristic parameter extraction method, it achieves smaller test error in sensor coarse signal processing mode of processing interference signal. We evaluate the proposed approach on a set of data. The experimental results show that, the proposed approach is able to improve the performance in different experimental setting

  9. Step Characterization using Sensor Information Fusion and Machine Learning

    Directory of Open Access Journals (Sweden)

    Ricardo Anacleto

    2015-12-01

    Full Text Available A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

  10. Evolutionary Adaptive Discovery of Phased Array Sensor Signal Identification

    Energy Technology Data Exchange (ETDEWEB)

    Timothy R. McJunkin; Milos Manic

    2011-05-01

    Tomography, used to create images of the internal properties and features of an object, from phased array ultasonics is improved through many sophisiticated methonds of post processing of data. One approach used to improve tomographic results is to prescribe the collection of more data, from different points of few so that data fusion might have a richer data set to work from. This approach can lead to rapid increase in the data needed to be stored and processed. It also does not necessarily lead to have the needed data. This article describes a novel approach to utilizing the data aquired as a basis for adapting the sensors focusing parameters to locate more precisely the features in the material: specifically, two evolutionary methods of autofocusing on a returned signal are coupled with the derivations of the forumulas for spatially locating the feature are given. Test results of the two novel methods of evolutionary based focusing (EBF) illustrate the improved signal strength and correction of the position of feature using the optimized focal timing parameters, called Focused Delay Identification (FoDI).

  11. A radiosonde using a humidity sensor array with a platinum resistance heater and multi-sensor data fusion.

    Science.gov (United States)

    Shi, Yunbo; Luo, Yi; Zhao, Wenjie; Shang, Chunxue; Wang, Yadong; Chen, Yinsheng

    2013-07-12

    This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS) technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

  12. A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Yadong Wang

    2013-07-01

    Full Text Available This paper describes the design and implementation of a radiosonde which can measure the meteorological temperature, humidity, pressure, and other atmospheric data. The system is composed of a CPU, microwave module, temperature sensor, pressure sensor and humidity sensor array. In order to effectively solve the humidity sensor condensation problem due to the low temperatures in the high altitude environment, a capacitive humidity sensor including four humidity sensors to collect meteorological humidity and a platinum resistance heater was developed using micro-electro-mechanical-system (MEMS technology. A platinum resistance wire with 99.999% purity and 0.023 mm in diameter was used to obtain the meteorological temperature. A multi-sensor data fusion technique was applied to process the atmospheric data. Static and dynamic experimental results show that the designed humidity sensor with platinum resistance heater can effectively tackle the sensor condensation problem, shorten response times and enhance sensitivity. The humidity sensor array can improve measurement accuracy and obtain a reliable initial meteorological humidity data, while the multi-sensor data fusion technique eliminates the uncertainty in the measurement. The radiosonde can accurately reflect the meteorological changes.

  13. Application of D-S Evidence Fusion Method in the Fault Detection of Temperature Sensor

    Directory of Open Access Journals (Sweden)

    Zheng Dou

    2014-01-01

    Full Text Available Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault.

  14. Fast obstacle detection based on multi-sensor information fusion

    Science.gov (United States)

    Lu, Linli; Ying, Jie

    2014-11-01

    Obstacle detection is one of the key problems in areas such as driving assistance and mobile robot navigation, which cannot meet the actual demand by using a single sensor. A method is proposed to realize the real-time access to the information of the obstacle in front of the robot and calculating the real size of the obstacle area according to the mechanism of the triangle similarity in process of imaging by fusing datum from a camera and an ultrasonic sensor, which supports the local path planning decision. In the part of image analyzing, the obstacle detection region is limited according to complementary principle. We chose ultrasonic detection range as the region for obstacle detection when the obstacle is relatively near the robot, and the travelling road area in front of the robot is the region for a relatively-long-distance detection. The obstacle detection algorithm is adapted from a powerful background subtraction algorithm ViBe: Visual Background Extractor. We extracted an obstacle free region in front of the robot in the initial frame, this region provided a reference sample set of gray scale value for obstacle detection. Experiments of detecting different obstacles at different distances respectively, give the accuracy of the obstacle detection and the error percentage between the calculated size and the actual size of the detected obstacle. Experimental results show that the detection scheme can effectively detect obstacles in front of the robot and provide size of the obstacle with relatively high dimensional accuracy.

  15. System Dynamics and Adaptive Control for MEMS Gyroscope Sensor

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2011-01-01

    Full Text Available This paper presents an adaptive control approach for Micro-Electro-Mechanical Systems (MEMS z-axis gyroscope sensor. The dynamical model of MEMS gyroscope sensor is derived and adaptive state tracking control for MEMS gyroscope is developed. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms due to the fabrication imperfection. The stability of the closed-loop systems is established with the proposed adaptive control strategy. Numerical simulation is investigated to verify the effectiveness of the proposed control scheme.

  16. Signal coupling to embedded pitch adapters in silicon sensors

    CERN Document Server

    Artuso, Marina; Bezshyiko, Iaroslava; Blusk, Steven R.; Brundler Denzer, Ruth; Bugiel, Szymon; Dasgupta, Roma; Dendek, Adam Mateusz; Dey, Biplab; Ely, Scott Edward; Lionetto, Federica; Petruzzo, Marco; Polyakov, Ivan; Rudolph, Matthew Scott; Schindler, Heinrich; Steinkamp, Olaf; Stone, Sheldon

    2017-01-01

    We have examined the effects of embedded pitch adapters on signal formation in n-substrate silicon microstrip sensors with data from beam tests and simulation. According to simulation, the presence of the pitch adapter metal layer changes the electric field inside the sensor, resulting in slowed signal formation on the nearby strips and a pick-up effect on the pitch adapter. This can result in an inefficiency to detect particles passing through the pitch adapter region. All these effects have been observed in the beam test data.

  17. A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection.

    NARCIS (Netherlands)

    Schutte, K.; Schavemaker, J.G.M.; Cremer, F.; Breejen, E. den

    2001-01-01

    We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The syste

  18. Infrared processing and sensor fusion for anti-personnel land-mine detection

    NARCIS (Netherlands)

    Schavemaker, J.G.M.; Cremer, F.; Schutte, K.; Breejen, E. den

    2000-01-01

    In this paper we present the results of infrared processing and sensor fusion obtained within the European research project GEODE (Ground Explosive Ordnance DEtection) that strives for the realization of a vehicle-mounted, multi-sensor anti-personnel land-mine detection system for humanitarian demin

  19. A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection.

    NARCIS (Netherlands)

    Schutte, K.; Schavemaker, J.G.M.; Cremer, F.; Breejen, E. den

    2001-01-01

    We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The

  20. Detecting Pedestrian Flocks by Fusion of Multi-Modal Sensors in Mobile Phones

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun; Wirz, Martin; Roggen, Daniel

    2012-01-01

    derived from multiple sensor modalities of modern smartphones. Automatic detection of flocks has several important applications, including evacuation management and socially aware computing. The novelty of this paper is, firstly, to use data fusion techniques to combine several sensor modalities (Wi...

  1. Distributed fusion estimation for sensor networks with communication constraints

    CERN Document Server

    Zhang, Wen-An; Song, Haiyu; Yu, Li

    2016-01-01

    This book systematically presents energy-efficient robust fusion estimation methods to achieve thorough and comprehensive results in the context of network-based fusion estimation. It summarizes recent findings on fusion estimation with communication constraints; several novel energy-efficient and robust design methods for dealing with energy constraints and network-induced uncertainties are presented, such as delays, packet losses, and asynchronous information... All the results are presented as algorithms, which are convenient for practical applications.

  2. Fusion of airborne radar and FLIR sensors for runway incursion detection

    Science.gov (United States)

    White, Joseph H.; Haidt, James G.; Britt, Charles L.; Archer, Cynthia; Neece, Robert T.

    2009-08-01

    Forward looking infrared and Radar (X-band or Ku-band) sensors are potential components in external hazard monitoring systems for general aviation aircraft. We are investigating the capability of these sensors to provide hazard information to the pilot when normal visibility is reduced by meteorological conditions. Fusing detection results from FLIR and Radar sensors can improve hazard detection performance. We have developed a demonstration fusion system for the detection of runway incursions. In this paper, we present our fusion system, along with detection results from data recorded on approach to a landing during clear daylight, overcast daylight, and clear night conditions.

  3. Phase 1 report on sensor technology, data fusion and data interpretation for site characterization

    Energy Technology Data Exchange (ETDEWEB)

    Beckerman, M.

    1991-10-01

    In this report we discuss sensor technology, data fusion and data interpretation approaches of possible maximal usefulness for subsurface imaging and characterization of land-fill waste sites. Two sensor technologies, terrain conductivity using electromagnetic induction and ground penetrating radar, are described and the literature on the subject is reviewed. We identify the maximum entropy stochastic method as one providing a rigorously justifiable framework for fusing the sensor data, briefly summarize work done by us in this area, and examine some of the outstanding issues with regard to data fusion and interpretation. 25 refs., 17 figs.

  4. Statistical sensor fusion of ECG data using automotive-grade sensors

    Science.gov (United States)

    Koenig, A.; Rehg, T.; Rasshofer, R.

    2015-11-01

    Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

  5. Gesture-Directed Sensor-Information Fusion for Communication in Hazardous Environments

    Science.gov (United States)

    2010-06-01

    sensors for gesture recognition [1], [2]. An important future step to enhance the effectiveness of the war fighter is to integrate CBRN and other...addition to the standard eGlove magnetic and motion gesture recognition sensors. War fighters progressing through a battlespace are now providing...a camera for gesture recognition is absolutely not an option for a CBRN war fighter in a battlefield scenario. Multi sensor fusion is commonly

  6. Computational Complexity Comparison Of Multi-Sensor Single Target Data Fusion Methods By Matlab

    OpenAIRE

    Hoseini, Sayed Amir; Ashraf, Mohammad Reza

    2013-01-01

    Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and o...

  7. Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

    Science.gov (United States)

    V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

    2016-12-01

    The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

  8. Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking.

    Science.gov (United States)

    Ligorio, Gabriele; Bergamini, Elena; Pasciuto, Ilaria; Vannozzi, Giuseppe; Cappozzo, Aurelio; Sabatini, Angelo Maria

    2016-01-26

    Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor's uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process.

  9. Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Bai

    2015-04-01

    Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

  10. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis.

    Science.gov (United States)

    Jiang, Wen; Xie, Chunhe; Zhuang, Miaoyan; Shou, Yehang; Tang, Yongchuan

    2016-09-15

    Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster-Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.

  11. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Wen Jiang

    2016-09-01

    Full Text Available Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B, can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.

  12. Information Fusion of Online Oil Monitoring System Using Multiple Sensors

    Institute of Scientific and Technical Information of China (English)

    高慧良; 周新聪; 程海明; 赵春华; 严新平

    2004-01-01

    Machine lubrication contains abundant information on the equipment operation.Nowadays, most measuring methods are based on offline sampling or on online measuring with a single sensor.An online oil monitoring system with multiple sensors was designed.The measurement data was processed with a fuzzy intelligence system.Information from integrated sensors in an oil online monitoring system was evaluated using fuzzy logic.The analyses show that the multiple sensors evaluation results are more reliable than online monitoring systems with single sensors.

  13. Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking

    Directory of Open Access Journals (Sweden)

    Gabriele Ligorio

    2016-01-01

    Full Text Available Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor’s uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process.

  14. JDL level 0 and 1 algorithms for processing and fusion of hard sensor data

    Science.gov (United States)

    Rimland, Jeffrey C.; Iyer, Ganesh M.; Agumamidi, Rachana R.; Pisupati, Soumya V.; Graham, Jake

    2011-06-01

    A current trend in information fusion involves distributed methods of combining both conventional "hard" sensor data and human-based "soft" information in a manner that exploits the most useful and accurate capabilities of each modality. In addition, new and evolving technologies such as Flash LIDAR have greatly enhanced the ability of a single device to rapidly sense attributes of a scene in ways that were not previously possible. At the Pennsylvania State University we are participating in a multi-disciplinary university research initiative (MURI) program funded by the U.S. Army Research Office to investigate issues related to fusing hard and soft data in counterinsurgency (COIN) situations. We are developing level 0 and level 1 methods (using the Joint Directors of Laboratories (JDL) data fusion process model) for fusion of physical ("hard") sensor data. Techniques include methods for data alignment, tracking, recognition, and identification for a sensor suite that includes LIDAR, multi-camera systems, and acoustic sensors. The goal is to develop methods that dovetail on-going research in soft sensor processing. This paper describes various hard sensor processing algorithms and their evolving roles and implementations within a distributed hard and soft information fusion system.

  15. Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks.

    Science.gov (United States)

    Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng

    2016-05-30

    Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity.

  16. Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

    Science.gov (United States)

    Rand, Robert S.; Khuon, Timothy; Truslow, Eric

    2016-07-01

    A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus of University of Southern Mississippi, Gulfport, Mississippi.

  17. Adaptive computational resource allocation for sensor networks

    Institute of Scientific and Technical Information of China (English)

    WANG Dian-hong; FEI E; YAN Yu-jie

    2008-01-01

    To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.

  18. All Source Adaptive Fusion for Aided Navigation in Non-GPS Environment

    National Research Council Canada - National Science Library

    Klausutis, Timothy J; Wehling, Ric; Lames, Matthew; Aboutalib, Omar; Awalt, Bruce; Fund, Alex; Thai, Bea; Leibs, Jeremy

    2007-01-01

    An innovative approach for navigation in non-GPS environment is presented based on all source adaptive fusion of any available information encompassing passive imaging data, digital elevation terrain...

  19. Performance evaluation of multi-sensor data-fusion systems in launch vehicles

    Indian Academy of Sciences (India)

    B N Suresh; K Sivan

    2004-04-01

    In this paper, the utilization of multi-sensors of different types, their characteristics, and their data-fusion in launch vehicles to achieve the goal of injecting the satellite into a precise orbit is explained. Performance requirements of sensors and their redundancy management in a typical launch vehicle are also included. The role of an integrated system level-test bed for evaluating multi-sensors and mission performance in a typical launch vehicle mission is described. Some of the typical simulation results to evaluate the effect of the sensors on the overall system are highlighted.

  20. A fusion method that performs better than best sensor

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.

    1998-03-01

    In multiple sensor systems, it is generally known that a good fuser will outperform the best sensor, and on the other hand, an inappropriate fuser can perform worse than the worst sensor. If the error distributions of the sensors are precisely known, an optimal fuser--that performs at least as well as best sensor--can be designed using statistical estimation methods. In engineering and robotic systems, however, it is too difficult and expensive to derive closed form error distributions required by these methods. This problem is further compounded by the variety and complexity of present day sensor systems, wherein a number of sensing hardware units and computing modules could be integrated into a single sensor. In these systems, however, it is possible to collect sensor data by sensing objects with known parameters. Thus, it is very important to have sample-based methods that enable a fuser to perform at least as well as the best sensor. In this paper, the author presents a generic analytical formulation of this problem, and provide a very simple property that yields such fuser.

  1. A sensor fusion method for Wi-Fi-based indoor positioning

    Directory of Open Access Journals (Sweden)

    Dongsoo Han

    2016-06-01

    Full Text Available This paper presents a sensor fusion method for a Wi-Fi-based indoor positioning system, named the KAist Indoor LOcating System (KAILOS, which was developed to realize a global indoor positioning system (GIPS that utilizes crowd-sourced fingerprints. KAILOS supports the deployment of indoor positioning systems in buildings by collecting indoor maps and fingerprint DBs of buildings for the GIPS. Thereby, KAILOS provides a method based on sensor fusion for volunteers to develop indoor positioning systems for their buildings. KAILOS has been made available online for public use. In addition, various location-based applications can also be developed using KAILOS.

  2. Autonomous navigation vehicle system based on robot vision and multi-sensor fusion

    Science.gov (United States)

    Wu, Lihong; Chen, Yingsong; Cui, Zhouping

    2011-12-01

    The architecture of autonomous navigation vehicle based on robot vision and multi-sensor fusion technology is expatiated in this paper. In order to acquire more intelligence and robustness, accurate real-time collection and processing of information are realized by using this technology. The method to achieve robot vision and multi-sensor fusion is discussed in detail. The results simulated in several operating modes show that this intelligent vehicle has better effects in barrier identification and avoidance and path planning. And this can provide higher reliability during vehicle running.

  3. Sensor fusion to enable next generation low cost Night Vision systems

    Science.gov (United States)

    Schweiger, R.; Franz, S.; Löhlein, O.; Ritter, W.; Källhammer, J.-E.; Franks, J.; Krekels, T.

    2010-04-01

    The next generation of automotive Night Vision Enhancement systems offers automatic pedestrian recognition with a performance beyond current Night Vision systems at a lower cost. This will allow high market penetration, covering the luxury as well as compact car segments. Improved performance can be achieved by fusing a Far Infrared (FIR) sensor with a Near Infrared (NIR) sensor. However, fusing with today's FIR systems will be too costly to get a high market penetration. The main cost drivers of the FIR system are its resolution and its sensitivity. Sensor cost is largely determined by sensor die size. Fewer and smaller pixels will reduce die size but also resolution and sensitivity. Sensitivity limits are mainly determined by inclement weather performance. Sensitivity requirements should be matched to the possibilities of low cost FIR optics, especially implications of molding of highly complex optical surfaces. As a FIR sensor specified for fusion can have lower resolution as well as lower sensitivity, fusing FIR and NIR can solve performance and cost problems. To allow compensation of FIR-sensor degradation on the pedestrian detection capabilities, a fusion approach called MultiSensorBoosting is presented that produces a classifier holding highly discriminative sub-pixel features from both sensors at once. The algorithm is applied on data with different resolution and on data obtained from cameras with varying optics to incorporate various sensor sensitivities. As it is not feasible to record representative data with all different sensor configurations, transformation routines on existing high resolution data recorded with high sensitivity cameras are investigated in order to determine the effects of lower resolution and lower sensitivity to the overall detection performance. This paper also gives an overview of the first results showing that a reduction of FIR sensor resolution can be compensated using fusion techniques and a reduction of sensitivity can be

  4. Data fusion on a distributed heterogeneous sensor network.

    Energy Technology Data Exchange (ETDEWEB)

    Lamborn, Peter; Williams, Pamela J.

    2006-02-01

    Alarm-based sensor systems are being explored as a tool to expand perimeter security for facilities and force protection. However, the collection of increased sensor data has resulted in an insufficient solution that includes faulty data points. Data analysis is needed to reduce nuisance and false alarms, which will improve officials decision making and confidence levels in the system's alarms. Moreover, operational costs can be allayed and losses mitigated if authorities are alerted only when a real threat is detected. In the current system, heuristics such as persistence of alarm and type of sensor that detected an event are used to guide officials responses. We hypothesize that fusing data from heterogeneous sensors in the sensor field can provide more complete situational awareness than looking at individual sensor data. We propose a two stage approach to reduce false alarms. First, we use self organizing maps to cluster sensors based on global positioning coordinates and then train classifiers on the within cluster data to obtain a local view of the event. Next, we train a classifier on the local results to compute a global solution. We investigate the use of machine learning techniques, such as k-nearest neighbor, neural networks, and support vector machines to improve alarm accuracy. On simulated sensor data, the proposed approach identifies false alarms with greater accuracy than a weighted voting algorithm.

  5. STUDY ON THE COAL-ROCK INTER-FACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

    Institute of Scientific and Technical Information of China (English)

    Ren Fang; Yang Zhaojian; Xiong Shibo

    2003-01-01

    The coal-rock interface recognition method based on multi-sensor data fusion technique is put forward because of the localization of single type sensor recognition method. The measuring theory based on multi-sensor data fusion technique is analyzed, and hereby the test platform of recognition system is manufactured. The advantage of data fusion with the fuzzy neural network (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carried out. The experiments show that in various conditions the method can always acquire a much higher recognition rate than normal ones.

  6. Comparison of pH Data Measured with a pH Sensor Array Using Different Data Fusion Methods

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liao

    2012-09-01

    Full Text Available This paper introduces different data fusion methods which are used for an electrochemical measurement using a sensor array. In this study, we used ruthenium dioxide sensing membrane pH electrodes to form a sensor array. The sensor array was used for detecting the pH values of grape wine, generic cola drink and bottled base water. The measured pH data were used for data fusion methods to increase the reliability of the measured results, and we also compared the fusion results with other different data fusion methods.

  7. Advanced data visualization and sensor fusion: Conversion of techniques from medical imaging to Earth science

    Science.gov (United States)

    Savage, Richard C.; Chen, Chin-Tu; Pelizzari, Charles; Ramanathan, Veerabhadran

    1993-01-01

    Hughes Aircraft Company and the University of Chicago propose to transfer existing medical imaging registration algorithms to the area of multi-sensor data fusion. The University of Chicago's algorithms have been successfully demonstrated to provide pixel by pixel comparison capability for medical sensors with different characteristics. The research will attempt to fuse GOES (Geostationary Operational Environmental Satellite), AVHRR (Advanced Very High Resolution Radiometer), and SSM/I (Special Sensor Microwave Imager) sensor data which will benefit a wide range of researchers. The algorithms will utilize data visualization and algorithm development tools created by Hughes in its EOSDIS (Earth Observation SystemData/Information System) prototyping. This will maximize the work on the fusion algorithms since support software (e.g. input/output routines) will already exist. The research will produce a portable software library with documentation for use by other researchers.

  8. Developing a Model for Simplified Higher Level Sensor Fusion

    Science.gov (United States)

    2013-01-01

    conveying its wide scope is to use a process model. The most referenced model within the DoD appears to be the Joint Director of Labs ( JDL ) data fusion...model shown in Figure 1 [5]. The JDL , is an organization which no longer exists but in the 1980s they were tasked to develop a model for data fu- sion...This JDL model, revised in 1999, was created to show a general process of data fusion with wide applicability for both government and academia. It

  9. Adaptive and mobile ground sensor array.

    Energy Technology Data Exchange (ETDEWEB)

    Holzrichter, Michael Warren; O' Rourke, William T.; Zenner, Jennifer; Maish, Alexander B.

    2003-12-01

    The goal of this LDRD was to demonstrate the use of robotic vehicles for deploying and autonomously reconfiguring seismic and acoustic sensor arrays with high (centimeter) accuracy to obtain enhancement of our capability to locate and characterize remote targets. The capability to accurately place sensors and then retrieve and reconfigure them allows sensors to be placed in phased arrays in an initial monitoring configuration and then to be reconfigured in an array tuned to the specific frequencies and directions of the selected target. This report reviews the findings and accomplishments achieved during this three-year project. This project successfully demonstrated autonomous deployment and retrieval of a payload package with an accuracy of a few centimeters using differential global positioning system (GPS) signals. It developed an autonomous, multisensor, temporally aligned, radio-frequency communication and signal processing capability, and an array optimization algorithm, which was implemented on a digital signal processor (DSP). Additionally, the project converted the existing single-threaded, monolithic robotic vehicle control code into a multi-threaded, modular control architecture that enhances the reuse of control code in future projects.

  10. Maneuvering Vehicle Tracking Based on Multi-sensor Fusion%基于多传感融合的路面机动目标跟踪

    Institute of Scientific and Technical Information of China (English)

    陈莹; 韩崇昭

    2005-01-01

    Maneuvering targets tracking is a fundamental task in intelligent vehicle research. This paper focuses on the problem of fusion between radar and image sensors in targets tracking. In order to improve positioning accuracy and narrow down the image working area, a novel method that integrates radar filter with image intensity is proposed to establish an adaptive vision window.A weighted Hausdorff distance is introduced to define the functional relationship between image and model projection, and a modified simulated annealing algorithm is used to find optimum orientation parameter. Furthermore, the global state is estimated, which refers to the distributed data fusion algorithm. Experiment results show that our method is accurate.

  11. Using Bayesian Programming for Multi-Sensor Data Fusion in Automotive Applications

    OpenAIRE

    Coué, Christophe; Fraichard, Thierry; Bessiere, Pierre; Mazer, Emmanuel

    2002-01-01

    International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing sytem providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the in...

  12. An Adaptive Sensor Mining Framework for Pervasive Computing Applications

    Science.gov (United States)

    Rashidi, Parisa; Cook, Diane J.

    Analyzing sensor data in pervasive computing applications brings unique challenges to the KDD community. The challenge is heightened when the underlying data source is dynamic and the patterns change. We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model. In our framework, the frequent and periodic patterns of data are first discovered by the Frequent and Periodic Pattern Miner (FPPM) algorithm; and then any changes in the discovered patterns over the lifetime of the system are discovered by the Pattern Adaptation Miner (PAM) algorithm, in order to adapt to the changing environment. This framework also captures vital context information present in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

  13. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

    Directory of Open Access Journals (Sweden)

    Xiang He

    2015-12-01

    Full Text Available Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer, wireless signal strength indicators (WiFi, Bluetooth, Zigbee, and visual sensors (LiDAR, camera. People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

  14. Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

    Science.gov (United States)

    He, Xiang; Aloi, Daniel N; Li, Jia

    2015-12-14

    Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

  15. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

    Science.gov (United States)

    Zou, Tengyue; Li, Zhenjia; Li, Shuyuan; Lin, Shouying

    2017-05-04

    Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  16. Adaptive inferential sensors based on evolving fuzzy models.

    Science.gov (United States)

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  17. Neural network implementations of data association algorithms for sensor fusion

    Science.gov (United States)

    Brown, Donald E.; Pittard, Clarence L.; Martin, Worthy N.

    1989-01-01

    The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.

  18. Health-Enabled Smart Sensor Fusion Technology Project

    Data.gov (United States)

    National Aeronautics and Space Administration — It has been proven that the combination of smart sensors with embedded metadata and wireless technologies present real opportunities for significant improvements in...

  19. Joint-FACET: The Canada-Netherlands initiative to study multi-sensor data fusion systems

    NARCIS (Netherlands)

    Bossee, E.; Theil, A.; Huizing, A.G.; Aartsen, C.S. van

    1998-01-01

    This paper presents the progress of a collaborative effort between Canada and The Netherlands in analyzing multi-sensor data fusion systems, e.g. for potential application to their respective frigates. In view of the overlapping interest in studying and comparing applicability and performance and ad

  20. Comparison of belief functions and voting method for fusion of mine detection sensors

    NARCIS (Netherlands)

    Milisavljevic, N.; Broek, S.P. van den; Bloch, I.; Schwering, P.B.W.; Lensen, H.A.; Acheroy, M.

    2001-01-01

    In this paper, two methods for fusion of mine detection sensors are presented, based on belief functions and on voting procedures, respectively. Their application is illustrated and compared on a real multisensor data set collected at the TNO test facilities under the HOM 2000 project. This set cont

  1. Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

    Directory of Open Access Journals (Sweden)

    Junhai Luo

    2014-01-01

    Log-likelihood Ratio Test (ELRT rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures.

  2. Detection of anti-personnel land-mines using sensor-fusion techniques

    NARCIS (Netherlands)

    Cremer, F.; Schavemaker, J.G.M.; Breejen, E. den; Schutte, K.

    1999-01-01

    In this paper we present the sensor-fusion results based on the measurements obtained within the European research project GEODE (Ground Explosive Ordnance DEtection system) that strives for the realisation of a vehicle-mounted, multisensor, anti-personnel land-mine detection system for humanitarian

  3. Botnet Detection Architecture Based on Heterogeneous Multi-sensor Information Fusion

    Directory of Open Access Journals (Sweden)

    HaiLong Wang

    2011-12-01

    Full Text Available As technology has been developed rapidly, botnet threats to the global cyber community are also increasing. And the botnet detection has recently become a major research topic in the field of network security. Most of the current detection approaches work only on the evidence from single information source, which can not hold all the traces of botnet and hardly achieve high accuracy. In this paper, a novel botnet detection architecture based on heterogeneous multi-sensor information fusion is proposed. The architecture is designed to carry out information integration in the three fusion levels of data, feature, and decision. As the core component, a feature extraction module is also elaborately designed. And an extended algorithm of the Dempster-Shafer (D-S theory is proved and adopted in decision fusion. Furthermore, a representative case is provided to illustrate that the detection architecture can effectively fuse the complicated information from various sensors, thus to achieve better detection effect.

  4. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

  5. Fusion: ultra-high-speed and IR image sensors

    Science.gov (United States)

    Etoh, T. Goji; Dao, V. T. S.; Nguyen, Quang A.; Kimata, M.

    2015-08-01

    Most targets of ultra-high-speed video cameras operating at more than 1 Mfps, such as combustion, crack propagation, collision, plasma, spark discharge, an air bag at a car accident and a tire under a sudden brake, generate sudden heat. Researchers in these fields require tools to measure the high-speed motion and heat simultaneously. Ultra-high frame rate imaging is achieved by an in-situ storage image sensor. Each pixel of the sensor is equipped with multiple memory elements to record a series of image signals simultaneously at all pixels. Image signals stored in each pixel are read out after an image capturing operation. In 2002, we developed an in-situ storage image sensor operating at 1 Mfps 1). However, the fill factor of the sensor was only 15% due to a light shield covering the wide in-situ storage area. Therefore, in 2011, we developed a backside illuminated (BSI) in-situ storage image sensor to increase the sensitivity with 100% fill factor and a very high quantum efficiency 2). The sensor also achieved a much higher frame rate,16.7 Mfps, thanks to the wiring on the front side with more freedom 3). The BSI structure has another advantage that it has less difficulties in attaching an additional layer on the backside, such as scintillators. This paper proposes development of an ultra-high-speed IR image sensor in combination of advanced nano-technologies for IR imaging and the in-situ storage technology for ultra-highspeed imaging with discussion on issues in the integration.

  6. A New, Adaptable, Optical High-Resolution 3-Axis Sensor

    Directory of Open Access Journals (Sweden)

    Niels Buchhold

    2017-01-01

    Full Text Available This article presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g., a laser diode, is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD. The downstream microcontroller’s software identifies the geometric shape’s center, distortion and size, and then calculates x, y, and z coordinates, which can be processed in attached devices. Depending on the image sensor in use (e.g., 6.41 megapixels, the 3-axis sensor features a resolution of 1544 digits from right to left and 1038 digits up and down. Through interpolation, these values rise by a factor of 100. A unique feature is the exact reproducibility (deflection to coordinates and its precise ability to return to its neutral position. Moreover, optical signal processing provides a high level of protection against electromagnetic and radio frequency interference. The sensor is adaptive and adjustable to fit a user’s range of motion (stroke and force. This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.

  7. A New, Adaptable, Optical High-Resolution 3-Axis Sensor.

    Science.gov (United States)

    Buchhold, Niels; Baumgartner, Christian

    2017-01-27

    This article presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g., a laser diode, is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD). The downstream microcontroller's software identifies the geometric shape's center, distortion and size, and then calculates x, y, and z coordinates, which can be processed in attached devices. Depending on the image sensor in use (e.g., 6.41 megapixels), the 3-axis sensor features a resolution of 1544 digits from right to left and 1038 digits up and down. Through interpolation, these values rise by a factor of 100. A unique feature is the exact reproducibility (deflection to coordinates) and its precise ability to return to its neutral position. Moreover, optical signal processing provides a high level of protection against electromagnetic and radio frequency interference. The sensor is adaptive and adjustable to fit a user's range of motion (stroke and force). This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.

  8. A New, Adaptable, Optical High-Resolution 3-Axis Sensor

    Science.gov (United States)

    Buchhold, Niels; Baumgartner, Christian

    2017-01-01

    This article presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g., a laser diode, is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD). The downstream microcontroller’s software identifies the geometric shape’s center, distortion and size, and then calculates x, y, and z coordinates, which can be processed in attached devices. Depending on the image sensor in use (e.g., 6.41 megapixels), the 3-axis sensor features a resolution of 1544 digits from right to left and 1038 digits up and down. Through interpolation, these values rise by a factor of 100. A unique feature is the exact reproducibility (deflection to coordinates) and its precise ability to return to its neutral position. Moreover, optical signal processing provides a high level of protection against electromagnetic and radio frequency interference. The sensor is adaptive and adjustable to fit a user’s range of motion (stroke and force). This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability. PMID:28134824

  9. Embedded Relative Navigation Sensor Fusion Algorithms for Autonomous Rendezvous and Docking Missions

    Science.gov (United States)

    DeKock, Brandon K.; Betts, Kevin M.; McDuffie, James H.; Dreas, Christine B.

    2008-01-01

    bd Systems (a subsidiary of SAIC) has developed a suite of embedded relative navigation sensor fusion algorithms to enable NASA autonomous rendezvous and docking (AR&D) missions. Translational and rotational Extended Kalman Filters (EKFs) were developed for integrating measurements based on the vehicles' orbital mechanics and high-fidelity sensor error models and provide a solution with increased accuracy and robustness relative to any single relative navigation sensor. The filters were tested tinough stand-alone covariance analysis, closed-loop testing with a high-fidelity multi-body orbital simulation, and hardware-in-the-loop (HWIL) testing in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL).

  10. Automatic noncontact 3-dimensional gauging via sensor fusion

    Science.gov (United States)

    Buckley, Shawn; Tavormina, Joseph J.

    1993-09-01

    Manufacturers are now driving toward the increased use of automation and the goal of zero-defects. As quality is improved and defect rates approach the popularized " Six-Sigma" level (customarily 3. 4 defects per million) manual or sampled measurementtechniques limit the achievementof product quality and manufacturing cost objectives. New automated inspection and gaging technology is required for process verification and control. To be competitive in the current manufacturing environment new gaging technology must be integrated into the manufacturing process to provide on-line feedback. The co-authors are founders of CogniSense a technology company dedicated to industrial inspection and gaging applications which use non-contact sensing techniques. CogniSense is currently applying its technology in the precision metalforming and other manufacturing industries to perform automatic dimensional measurement and provide real time information used to control and fine-tune the manufacturing process. A variety of sensors are used to detect the characteristics of parts on-line as they are produced. Data from multiple sensors is " fused" and analyzed by a dedicated microcomputer which evaluates the sensory signature and calculates critical dimensions from the sensor input to determine whether parts are within the acceptable tolerance range. Pattern recognition algorithms are used to automatically select the sensors which provide the most important information about critical part characteristics and dimensions. These algorithms operate by observing the changes in sensor output as critical features of the part are varied. The decision-making algorithms

  11. An adaptive fusion strategy of polarization image based on NSCT

    Science.gov (United States)

    Zhao, Chang-xia; Duan, Jin; Mo, Chun-he; Chen, Guang-qiu; Fu, Qiang

    2015-03-01

    An improved image fusion algorithm based on the NSCT is proposed in this paper. After decomposition NSCT method of multi-scale and multiple directions, polarization image was decomposed into two parts: low frequency sub-band and high frequency band-pass images. The fusion strategy of combining local regional energy and gradient structure similarity were used in low-frequency coefficients. While in the high-frequency band-pass coefficients part, the fusion strategy of the location spatial frequency as the correlation coefficient was used. The intensity image and polarization degree image are fused for improving the sharpness and contrast of the image. The experiments show that the algorithm is effective to improve the imaging quality in the turbid medium.

  12. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    Directory of Open Access Journals (Sweden)

    Jesse S. Jin

    2010-10-01

    Full Text Available Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  13. Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

    Science.gov (United States)

    Li, Jiaming; Luo, Suhuai; Jin, Jesse S

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  14. Sensor fusion approaches for EMI and GPR-based subsurface threat identification

    Science.gov (United States)

    Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

    2011-06-01

    Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

  15. Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

    Directory of Open Access Journals (Sweden)

    Sang Won Yoon

    2015-01-01

    Full Text Available The Mecanum automated guided vehicle (AGV, which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter.

  16. Modelling and Simulation of Multi-target Multi-sensor Data Fusion for Trajectory Tracking

    Directory of Open Access Journals (Sweden)

    A.K. Singh

    2009-05-01

    Full Text Available An implementation of track fusion using various algorthims has been demonstrated . The sensor measurements of these targets are modelled using Kalman filter (KF and interacting multiple models (IMM filter. The joint probabilistic data association filter (JPDAF and neural network fusion (NNF algorithms were used for tracking multiple man-euvring targets. Track association and fusion algorithm are executed to get the fused track data for various scenarios, two sensors tracking a single target to three sensors tracking three targets, to evaluate the effects of multiple and dispersed sensors for single target, two targets, and multiple targets. The targets chosen were distantly spaced, closely spaced and crossing. Performance of different filters was compared and fused trajectory is found to be closer to the true target trajectory as compared to that for any of the sensor measurements of that target.Defence Science Journal, 2009, 59(3, pp.205-214, DOI:http://dx.doi.org/10.14429/dsj.59.1513

  17. Design of Liquid Level Measurement System Using Multi Sensor Data Fusion for Improved Characteristics and Fault Detection

    OpenAIRE

    SANTHOSH K V Shashank Kumar

    2016-01-01

    Online validation of multi sensor data fusion based liquid level measurement technique using capacitance level sensor and ultrasonic level sensor is implemented in this work. The objectives of the proposed work is to calibrate level measurement system by fusing the outputs of fuzzy sets of Capacitive Level Sensor (CLS) and Ultrasonic Level Sensor (ULS) such that (i) sensitivity and linearity should be improved as compared to ULS, (ii) reduction of nonlinear characteristics like offset and sat...

  18. All-IP-Ethernet architecture for real-time sensor-fusion processing

    Science.gov (United States)

    Hiraki, Kei; Inaba, Mary; Tezuka, Hiroshi; Tomari, Hisanobu; Koizumi, Kenichi; Kondo, Shuya

    2016-03-01

    Serendipter is a device that distinguishes and selects very rare particles and cells from huge amount of population. We are currently designing and constructing information processing system for a Serendipter. The information processing system for Serendipter is a kind of sensor-fusion system but with much more difficulties: To fulfill these requirements, we adopt All IP based architecture: All IP-Ethernet based data processing system consists of (1) sensor/detector directly output data as IP-Ethernet packet stream, (2) single Ethernet/TCP/IP streams by a L2 100Gbps Ethernet switch, (3) An FPGA board with 100Gbps Ethernet I/F connected to the switch and a Xeon based server. Circuits in the FPGA include 100Gbps Ethernet MAC, buffers and preprocessing, and real-time Deep learning circuits using multi-layer neural networks. Proposed All-IP architecture solves existing problem to construct large-scale sensor-fusion systems.

  19. Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

    Directory of Open Access Journals (Sweden)

    Changho Lee

    2013-03-01

    Full Text Available The International Civil Aviation Organization (ICAO has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS, Automatic Dependent Surveillance-Broadcast (ADS-B, multilateration (MLAT and wide-area multilateration (WAM systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

  20. Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

    Science.gov (United States)

    Cook, Brandon; Cohen, Kelly

    2017-01-01

    As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

  1. Fusion of Smartphone Motion Sensors for Physical Activity Recognition

    NARCIS (Netherlands)

    Shoaib, Muhammad; Bosch, Stephan; Durmaz Incel, Ozlem; Scholten, Hans; Havinga, Paul J.M.

    2014-01-01

    For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting r

  2. Fusion of smartphone motion sensors for physical activity recognition.

    Science.gov (United States)

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

    2014-06-10

    For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

  3. Fusion of Smartphone Motion Sensors for Physical Activity Recognition

    Directory of Open Access Journals (Sweden)

    Muhammad Shoaib

    2014-06-01

    Full Text Available For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role and an accelerometer (in a lead role has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized. We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

  4. Engineering of Sensor Network Structure for Dependable Fusion

    Science.gov (United States)

    2014-08-15

    design a novel sensor architecture that partitioned the overall design into two separate but interacting design spaces, (1) Information Space (IS...classification, Signal Processing (03 2012) Devesh K. Jha, Dheeraj S. Singh, S. Gupta, A. Ray. Fractal analysis of crack initiation in polycrystalline alloys

  5. Sensor fusion in head pose tracking for augmented reality

    NARCIS (Netherlands)

    Persa, S.F.

    2006-01-01

    The focus of this thesis is on studying diverse techniques, methods and sensors for position and orientation determination with application to augmented reality applications. In Chapter 2 we reviewed a variety of existing techniques and systems for position determination. From a practical point of v

  6. Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

    NARCIS (Netherlands)

    Pham, T.Q.; Van Vliet, L.J.; Schutte, K.

    2006-01-01

    We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to

  7. Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

    NARCIS (Netherlands)

    Pham, T.Q.; Vliet, L.J. van; Schutte, K.

    2006-01-01

    We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to

  8. Multi-sensor data fusion for measurement of complex freeform surfaces

    Science.gov (United States)

    Ren, M. J.; Liu, M. Y.; Cheung, C. F.; Yin, Y. H.

    2016-01-01

    Along with the rapid development of the science and technology in fields such as space optics, multi-scale enriched freeform surfaces are widely used to enhance the performance of the optical systems in both functionality and size reduction. Multi-sensor technology is considered as one of the promising methods to measure and characterize these surfaces at multiple scales. This paper presents a multi-sensor data fusion based measurement method to purposely extract the geometric information of the components with different scales which is used to establish a holistic geometry of the surface via data fusion. To address the key problems of multi-sensor data fusion, an intrinsic feature pattern based surface registration method is developed to transform the measured datasets to a common coordinate frame. Gaussian zero-order regression filter is then used to separate each measured data in different scales, and the datasets are fused based on an edge intensity data fusion algorithm within the same wavelength. The fused data at different scales is then merged to form a new surface with holistic multiscale information. Experimental study is presented to verify the effectiveness of the proposed method.

  9. DE-FE0013062 Final report PARC NETL DOE Heat sensor heat sensor harsh environment adaptable thermionic sensor

    Energy Technology Data Exchange (ETDEWEB)

    Limb, Scott J. [Palo Alto Research Center Inc., CA (United States)

    2016-05-31

    This document is the final report for the “HARSH ENVIRONMENT ADAPTABLE THERMIONIC SENSOR” project under NETL’s Crosscutting contract DE-FE0013062. This report addresses sensors that can be made with thermionic thin films along with the required high temperature hermetic packaging process. These sensors can be placed in harsh high temperature environments and potentially be wireless and self-powered.

  10. Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

    OpenAIRE

    Vyas, Nisarg; BodyMedia, Inc.; Farringdon, Jonathan; BodyMedia Inc.; Andre, David; Cerebellum Capital, Inc.; Stivoric, John Ivo; BodyMedia

    2012-01-01

    In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges...

  11. Decision and feature fusion over the fractal inference network using camera and range sensors

    Science.gov (United States)

    Erkmen, Ismet; Erkmen, Aydan M.; Ucar, Ekin

    1998-10-01

    The objective of the ongoing work is to fuse information from uncertain environmental data taken by cameras, short range sensors including infrared and ultrasound sensors for strategic target recognition and task specific action in Mobile Robot applications. Our present goal in this paper is to demonstrate target recognition for service robot in a simple office environment. It is proposed to fuse all sensory signals obtained from multiple sensors over a fully layer-connected sensor network system that provides an equal opportunity competitive environment for sensory data where those bearing less uncertainty, less complexity and less inconsistencies with the overall goal survive, while others fade out. In our work, this task is achieved as a decision fusion using the Fractal Inference Network (FIN), where information patterns or units--modeled as textured belief functions bearing a fractal dimension due to uncertainty-- propagate while being processed at the nodes of the network. Each local process of a node generates a multiresolutional feature fusion. In this model, the environment is observed by multisensors of different type, different resolution and different spatial location without a prescheduled sensing scenario in data gathering. Node activation and flow control of information over the FIN is performed by a neuro- controller, a concept that has been developed recently as an improvement over the classical Fractal Inference Network. In this paper, the mathematical closed form representation for decision fusion over the FIN is developed in a way suitable for analysis and is applied to a NOMAD mobile robot servicing an office environment.

  12. A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.

    Science.gov (United States)

    Tang, Yongchuan; Zhou, Deyun; Xu, Shuai; He, Zichang

    2017-04-22

    In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster-Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

  13. Design of Liquid Level Measurement System Using Multi Sensor Data Fusion for Improved Characteristics and Fault Detection

    Directory of Open Access Journals (Sweden)

    SANTHOSH K V Shashank Kumar

    2016-10-01

    Full Text Available Online validation of multi sensor data fusion based liquid level measurement technique using capacitance level sensor and ultrasonic level sensor is implemented in this work. The objectives of the proposed work is to calibrate level measurement system by fusing the outputs of fuzzy sets of Capacitive Level Sensor (CLS and Ultrasonic Level Sensor (ULS such that (i sensitivity and linearity should be improved as compared to ULS, (ii reduction of nonlinear characteristics like offset and saturation which persists in CLS, and (iii detection and identification of faults in sensors if any. These objectives are achieved by using the Joint Directors of Laboratories (JDL multi sensor data fusion framework in cascade to the outputs of both the sensor. The proposed liquid level measurement technique was subjected to testing with practical data and results show successful implementation of liquid level measurement system.

  14. Simple, High-Performance Fusion Rule for Censored Decisions in Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    LIU Xiangyang; PENG Yingning; WANG Xiutan

    2008-01-01

    Data selection-based summation fusion (DSSF) was developed to overcome the shortcomings of previously developed likelihood ratio tests based on channel statistics (LRT-CS) for the problem of fusing censored binary decisions transmitted over Nakagami fading channels in a wireless sensor network (WSN).The LRT-CS relies on detection probabilities of the local sensors, while the detection probabilities are a pri-ori unknown for uncooperative targets. Also, for Nakagami fading channels, the LRT-CS involves an infinite series, which is cumbersome for real-time application. In contrast, the DSSF only involves data comparisons and additions and does not require the detection probabilities of local sensors. Furthermore, the perform-ance of DSSF is only slightly degraded in comparison with the LRT-CS when the detection probabilities of local sensors are a priori unknown. Therefore, the DSSF should be used in a WSN with limited resources.

  15. Adaptive Optoelectronic Eyes: Hybrid Sensor/Processor Architectures

    Science.gov (United States)

    2006-11-13

    J.  Lange , C. von der Malsburg, R. P. Würtz, and W. Konen, “Distortion Invariant Object Recognition Adaptive Optoelectronic Eyes: Hybrid Sensor...Meeting, Dallas, Texas, (November, 1998). 17.  G. Sáry, G. Kovács, K. Köteles, G.  Benedek , J. Fiser, and I. Biederman, “Selectivity Variations in Monkey

  16. Wavefront sensors and algorithms for adaptive optical systems

    Science.gov (United States)

    Lukin, V. P.; Botygina, N. N.; Emaleev, O. N.; Konyaev, P. A.

    2010-07-01

    The results of recent works related to techniques and algorithms for wave-front (WF) measurement using Shack-Hartmann sensors show their high efficiency in solution of very different problems of applied optics. The goal of this paper was to develop a sensitive Shack-Hartmann sensor with high precision WF measurement capability on the base of modern technology of optical elements making and new efficient methods and computational algorithms of WF reconstruction. The Shack-Hartmann sensors sensitive to small WF aberrations are used for adaptive optical systems, compensating the wave distortions caused by atmospheric turbulence. A high precision Shack-Hartmann WF sensor has been developed on the basis of a low-aperture off-axis diffraction lens array. The device is capable of measuring WF slopes at array sub-apertures of size 640×640 μm with an error not exceeding 4.80 arcsec (0.15 pixel), which corresponds to the standard deviation equal to 0.017λ at the reconstructed WF with wavelength λ . Also the modification of this sensor for adaptive system of solar telescope using extended scenes as tracking objects, such as sunspot, pores, solar granulation and limb, is presented. The software package developed for the proposed WF sensors includes three algorithms of local WF slopes estimation (modified centroids, normalized cross-correlation and fast Fourierdemodulation), as well as three methods of WF reconstruction (modal Zernike polynomials expansion, deformable mirror response functions expansion and phase unwrapping), that can be selected during operation with accordance to the application.

  17. Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth

    Science.gov (United States)

    Alam, Mushfiqul; Rohac, Jan

    2015-01-01

    MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711

  18. A Multifunctional Joint Angle Sensor with Measurement Adaptability

    Directory of Open Access Journals (Sweden)

    Wei Quan

    2013-11-01

    Full Text Available The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.

  19. Initial Realization of a Sensor Fusion Based Onboard Maritime Integrated PNT Unit

    Directory of Open Access Journals (Sweden)

    Ralf Ziebold

    2013-03-01

    Full Text Available This paper introduces the basic concept of the Position Navigation and Timing (PNT Module as future part of a ship side Integrated Navigation System (INS. Core of the PNT Module is a sensor fusion based processing system (PNT Unit. The paper will focus on important aspects and first results of the initial practical realization of such a PNT Unit, including a realization of a Consistent Common Reference System (CCRS, GNSS/IMU tightly coupled positioning results as well as contingency performance of the inertial sensors.

  20. Indoor Positioning and Localisation System with Sensor Fusion : AN IMPLEMENTATION ON AN INDOOR AUTONOMOUS ROBOT AT ÅF

    OpenAIRE

    Ericsson, John-Eric; Eriksson, Daniel

    2014-01-01

    This thesis will present guidelines of how to select sensors and algorithms for indoor positioning and localisation systems with sensor fusion. These guidelines are based on an extensive theory and state of the art research. Different scenarios are presented to give some examples of proposed sensors and algorithms for certain applications. There are of course no right or wrong sensor combinations, but some factors are good to bear in mind when a system is designed. To give an example of the p...

  1. Multi-sensor radiation detection, imaging, and fusion

    Energy Technology Data Exchange (ETDEWEB)

    Vetter, Kai [Department of Nuclear Engineering, University of California, Berkeley, CA 94720 (United States); Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)

    2016-01-01

    Glenn Knoll was one of the leaders in the field of radiation detection and measurements and shaped this field through his outstanding scientific and technical contributions, as a teacher, his personality, and his textbook. His Radiation Detection and Measurement book guided me in my studies and is now the textbook in my classes in the Department of Nuclear Engineering at UC Berkeley. In the spirit of Glenn, I will provide an overview of our activities at the Berkeley Applied Nuclear Physics program reflecting some of the breadth of radiation detection technologies and their applications ranging from fundamental studies in physics to biomedical imaging and to nuclear security. I will conclude with a discussion of our Berkeley Radwatch and Resilient Communities activities as a result of the events at the Dai-ichi nuclear power plant in Fukushima, Japan more than 4 years ago. - Highlights: • .Electron-tracking based gamma-ray momentum reconstruction. • .3D volumetric and 3D scene fusion gamma-ray imaging. • .Nuclear Street View integrates and associates nuclear radiation features with specific objects in the environment. • Institute for Resilient Communities combines science, education, and communities to minimize impact of disastrous events.

  2. Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

    Science.gov (United States)

    Schenker, Paul S. (Editor)

    1992-01-01

    Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

  3. Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

    Science.gov (United States)

    Schenker, Paul S. (Editor)

    1992-01-01

    Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

  4. Sensor fusion and nonlinear prediction for anomalous event detection

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez, J.V.; Moore, K.R.; Elphic, R.C.

    1995-03-07

    The authors consider the problem of using the information from various time series, each one characterizing a different physical quantity, to predict the future state of the system and, based on that information, to detect and classify anomalous events. They stress the application of principal components analysis (PCA) to analyze and combine data from different sensors. They construct both linear and nonlinear predictors. In particular, for linear prediction the authors use the least-mean-square (LMS) algorithm and for nonlinear prediction they use both backpropagation (BP) networks and fuzzy predictors (FP). As an application, they consider the prediction of gamma counts from past values of electron and gamma counts recorded by the instruments of a high altitude satellite.

  5. Performance evaluation of multi-sensor data fusion technique for test range application

    Indian Academy of Sciences (India)

    Shrabani Bhattacharya; R Appavu Raj

    2004-04-01

    We have adopted the state-vector fusion technique for fusing multiple sensors track data to provide complete and precise trajectory information about the flight vehicle under test, for the purpose of flight safety monitoring and decisionmaking at Test Range. The present paper brings out the performance of the algorithm for different process noise and measurement noise using simulated as well as real track data.

  6. Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component

    DEFF Research Database (Denmark)

    Christiansen, Martin Peter; Jensen, Kjeld; Ellekilde, Lars-Peter

    Using the detected trees seen in gure 4(b) a localised SLAM map of the surroundings area, can be created an used to determine the localisation of the tractor. This kind of sensor-fusion is used, to keep the amount of prior information about outlay of the orchard to a minimum, so it can be used...... in orchards with dierent outlays of the trees....

  7. Resistive sensor and electromagnetic actuator for feedback stabilization of liquid metal walls in fusion reactors

    CERN Document Server

    Mirhoseini, S H M

    2016-01-01

    Liquid metal walls in fusion reactors will be subject to instabilities, turbulence, induced currents, error fields and temperature gradients that will make them locally bulge, thus entering in contact with the plasma, or deplete, hence exposing the underlying solid substrate. To prevent this, research has begun to actively stabilize static or flowing liquid metal layers by locally applying forces in feedback with thickness measurements. Here we present resistive sensors of liquid metal thickness and demonstrate jxB actuators, to locally control it.

  8. Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video.

    Science.gov (United States)

    Gunay, Osman; Toreyin, Behçet Ugur; Kose, Kivanc; Cetin, A Enis

    2012-05-01

    In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.

  9. Evolving RBF neural networks for adaptive soft-sensor design.

    Science.gov (United States)

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  10. Sensor Fusion of Position- and Micro-Sensors (MEMS) integrated in a Wireless Sensor Network for movement detection in landslide areas

    Science.gov (United States)

    Arnhardt, Christian; Fernández-Steeger, Tomas; Azzam, Rafig

    2010-05-01

    Monitoring systems in landslide areas are important elements of effective Early Warning structures. Data acquisition and retrieval allows the detection of movement processes and thus is essential to generate warnings in time. Apart from the precise measurement, the reliability of data is fundamental, because outliers can trigger false alarms and leads to the loss of acceptance of such systems. For the monitoring of mass movements and their risk it is important to know, if there is movement, how fast it is and how trustworthy is the information. The joint project "Sensorbased landslide early warning system" (SLEWS) deals with these questions, and tries to improve data quality and to reduce false alarm rates, due to the combination of sensor date (sensor fusion). The project concentrates on the development of a prototypic Alarm- and Early Warning system (EWS) for different types of landslides by using various low-cost sensors, integrated in a wireless sensor network (WSN). The network consists of numerous connection points (nodes) that transfer data directly or over other nodes (Multi-Hop) in real-time to a data collection point (gateway). From there all the data packages are transmitted to a spatial data infrastructure (SDI) for further processing, analyzing and visualizing with respect to end-user specifications. The ad-hoc characteristic of the network allows the autonomous crosslinking of the nodes according to existing connections and communication strength. Due to the independent finding of new or more stable connections (self healing) a breakdown of the whole system is avoided. The bidirectional data stream enables the receiving of data from the network but also allows the transfer of commands and pointed requests into the WSN. For the detection of surface deformations in landslide areas small low-cost Micro-Electro-Mechanical-Systems (MEMS) and positionsensors from the automobile industries, different industrial applications and from other measurement

  11. Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth

    Directory of Open Access Journals (Sweden)

    Mushfiqul Alam

    2015-02-01

    Full Text Available MEMS (micro-electro-mechanical system-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU, which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor’s behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.

  12. An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks.

    Science.gov (United States)

    Chen, Huayan; Zhang, Senlin; Liu, Meiqin; Zhang, Qunfei

    2017-04-27

    We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.

  13. Wrap-Around Out-the-Window Sensor Fusion System

    Science.gov (United States)

    Fox, Jeffrey; Boe, Eric A.; Delgado, Francisco; Secor, James B.; Clark, Michael R.; Ehlinger, Kevin D.; Abernathy, Michael F.

    2009-01-01

    The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.

  14. Fusion

    CERN Document Server

    Mahaffey, James A

    2012-01-01

    As energy problems of the world grow, work toward fusion power continues at a greater pace than ever before. The topic of fusion is one that is often met with the most recognition and interest in the nuclear power arena. Written in clear and jargon-free prose, Fusion explores the big bang of creation to the blackout death of worn-out stars. A brief history of fusion research, beginning with the first tentative theories in the early 20th century, is also discussed, as well as the race for fusion power. This brand-new, full-color resource examines the various programs currently being funded or p

  15. Novel adaptive laser scanning sensor for reverse engineering measurement

    Institute of Scientific and Technical Information of China (English)

    Zhao Ji; Ma Zi; Lin Na; Zhu Quanmin

    2007-01-01

    In this paper, a series of new techniques are used to optimize typical laser scanning sensor. The integrated prototype is compared with traditional approach to demonstrate the much improved performance. In the research and development, camera calibration is achieved by extracting characteristic points of the laser plane, so that the calibration efficiency is improved significantly. With feedback control of its intensity, the laser is automatically adjusted for different material. A modified algorithm is presented to improve the accuracy of laser stripe extraction. The fusion of data extracted from left and right camera is completed with re-sampling technique. The scanner is integrated with a robot arm and some other machinery for on-line measurement and inspection, which provides a flexible measurement tool for reverse engineering.

  16. Swarm Robot Control for Human Services and Moving Rehabilitation by Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Tresna Dewi

    2014-01-01

    Full Text Available A current trend in robotics is fusing different types of sensors having different characteristics to improve the performance of a robot system and also benefit from the reduced cost of sensors. One type of robot that requires sensor fusion for its application is the service robot. To achieve better performance, several service robots are preferred to work together, and, hence, this paper concentrates on swarm service robots. Swarm service mobile robots operating within a fixed area need to cope with dynamic changes in the environment, and they must also be capable of avoiding dynamic and static obstacles. This study applies sensor fusion and swarm concept for service mobile robots in human services and rehabilitation environment. The swarm robots follow the human moving trajectory to provide support to human moving and perform several tasks required in their living environment. This study applies a reference control and proportional-integral (PI control for the obstacle avoidance function. Various computer simulations are performed to verify the effectiveness of the proposed method.

  17. Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Peng; QI Wen-Juan; DENG Zi-Li

    2014-01-01

    This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

  18. An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

    Directory of Open Access Journals (Sweden)

    Bingfei Fan

    2017-05-01

    Full Text Available Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

  19. Position Estimation by Wearable Walking Navigation System for Visually Impaired with Sensor Fusion

    Science.gov (United States)

    Watanabe, Hiromi; Yamamoto, Yoshihiko; Tanzawa, Tsutomu; Kotani, Shinji

    A wearable walking navigation system without any special infrastructures has been developed to guide visually impaired. It is important to estimate a position correctly so that safe navigation can be realized. In our system, different sensor data are fused to estimate a pedestrian's position. An image processing system and a laser range finder were used to estimate the positions indoors. In this paper, we introduce the concept of “similarity” between map information and sensor data. This similarity is used to estimate the positions. Experimental results show that highly accurate position estimation can be achieved by sensor fusion. The positions in a linear passage were estimated using image processing data, and when the passage turns, the positions were estimated using LRF data.

  20. Workshop on adaptive grid methods for fusion plasmas

    Energy Technology Data Exchange (ETDEWEB)

    Wiley, J.C. [Univ. of Texas, Austin, TX (United States)

    1995-07-01

    The author describes a general `hp` finite element method with adaptive grids. The code was based on the work of Oden, et al. The term `hp` refers to the method of spatial refinement (h), in conjunction with the order of polynomials used as a part of the finite element discretization (p). This finite element code seems to handle well the different mesh grid sizes occuring between abuted grids with different resolutions.

  1. Adaptive high-frequency information fusion algorithm of radar and optical images

    Science.gov (United States)

    Wang, Yiding; Qin, Shuai

    2011-12-01

    An adaptive High-frequency Information Fusion Algorithm of Radar and Optical Images is proposed in this paper, in order to improve the resolution of the radar image and reserve more radar information. Firstly, Hough Transform is adopted in the process of low-resolution radar image and high-resolution optical image registration. The implicit linear information is extracted from two different heterogeneous images for better result. Then NSCT transform is used for decomposition and fusion. In different decomposition layers or in the same layer with different directions, fusion rules are adaptive for the high-frequency information of images. The ratio values of high frequency information entropy, variance, gradient and edge strength are calculated after NSCT decomposition. High frequency information entropy, variance, gradient or edge strength, which has the smallest ratio value, is selected as an optimal rule for regional fusion. High-frequency information of radar image could be better retained, at the same time the low-frequency information of optical image also could be remained. Experimental results showed that our approach performs better than those methods with single fusion rule.

  2. Multi-sensor fusion techniques for state estimation of micro air vehicles

    Science.gov (United States)

    Donavanik, Daniel; Hardt-Stremayr, Alexander; Gremillion, Gregory; Weiss, Stephan; Nothwang, William

    2016-05-01

    Aggressive flight of micro air vehicles (MAVs) in unstructured, GPS-denied environments poses unique challenges for estimation of vehicle pose and velocity due to the noise, delay, and drift in individual sensor measurements. Maneuvering flight at speeds in excess of 5 m/s poses additional challenges even for active range sensors; in the case of LIDAR, an assembled scan of the vehicles environment will in most cases be obsolete by the time it is processed. Multi-sensor fusion techniques which combine inertial measurements with passive vision techniques and/or LIDAR have achieved breakthroughs in the ability to maintain accurate state estimates without the use of external positioning sensors. In this paper, we survey algorithmic approaches to exploiting sensors with a wide range of nonlinear dynamics using filter and bundle-adjustment based approaches for state estimation and optimal control. From this foundation, we propose a biologically-inspired framework for incorporating the human operator in the loop as a privileged sensor in a combined human/autonomy paradigm.

  3. RGB-D, Laser and Thermal Sensor Fusion for People following in a Mobile Robot

    Directory of Open Access Journals (Sweden)

    Loreto Susperregi

    2013-06-01

    Full Text Available Detecting and tracking people is a key capability for robots that operate in populated environments. In this paper, we used a multiple sensor fusion approach that combines three kinds of sensors in order to detect people using RGB-D vision, lasers and a thermal sensor mounted on a mobile platform. The Kinect sensor offers a rich data set at a significantly low cost, however, there are some limitations to its use in a mobile platform, mainly that the Kinect algorithms for people detection rely on images captured by a static camera. To cope with these limitations, this work is based on the combination of the Kinect and a Hokuyo laser and a thermopile array sensor. A real-time particle filter system merges the information provided by the sensors and calculates the position of the target, using probabilistic leg and thermal patterns, image features and optical flow to this end. Experimental results carried out with a mobile platform in a Science museum have shown that the combination of different sensory cues increases the reliability of the people following system.

  4. Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines

    Science.gov (United States)

    Er, Poi Voon; Teo, Chek Sing; Tan, Kok Kiong

    2016-02-01

    Moving mechanical parts in a machine will inevitably generate vibration profiles reflecting its operating conditions. Vibration profile analysis is a useful tool for real-time condition monitoring to avoid loss of performance and unwanted machine downtime. In this paper, we propose and validate an approach for sensor placement, selection and fusion for continuous machine condition monitoring. The main idea is to use a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it is not suitable to mount a sensor. The locations for sensors' mountings which are subsequently used for vibration inference are identified based on sensitivity calibration at these locations moderated with normalized Fisher Information (NFI) associated with the measurement quality of the sensor at that location. Each of the identified sensor placement location is associated with one or more sensitive frequencies for which it ranks top in terms of the moderated sensitivities calibrated. A set of Radial Basis Function (RBF), each of them associated with a range of sensitive frequencies, is used to infer the vibration at the critical point for that frequency. The overall vibration spectrum of the critical point is then fused from these components. A comprehensive set of experimental results for validation of the proposed approach is provided in the paper.

  5. RGB-D, Laser and Thermal Sensor Fusion for People Following in a Mobile Robot

    Directory of Open Access Journals (Sweden)

    Loreto Susperregi

    2013-06-01

    Full Text Available Detecting and tracking people is a key capability for robots that operate in populated environments. In this paper, we used a multiple sensor fusion approach that combines three kinds of sensors in order to detect people using RGB-D vision, lasers and a thermal sensor mounted on a mobile platform. The Kinect sensor offers a rich data set at a significantly low cost, however, there are some limitations to its use in a mobile platform, mainly that the Kinect algorithms for people detection rely on images captured by a static camera. To cope with these limitations, this work is based on the combination of the Kinect and a Hokuyo laser and a thermopile array sensor. A real-time particle filter system merges the information provided by the sensors and calculates the position of the target, using probabilistic leg and thermal patterns, image features and optical flow to this end. Experimental results carried out with a mobile platform in a Science museum have shown that the combination of different sensory cues increases the reliability of the people following system.

  6. Adaptive optical design in surface plasma resonance sensor

    Institute of Scientific and Technical Information of China (English)

    ZHANG Feng; ZHONG Jin-gang

    2006-01-01

    A double-prism adaptive optical design in surface plasma resonance (SPR) sensor is proposed,which consists of two identical isosceles right-triangular prisms. One prism is used as a component of Kretschmann configuration,and the other is for regulation of the optical path. When double-prism structure is angle-scanned by an immovable incident ray,the output ray will be always parallel with the incident ray and just has a small displacement with the shift of output point.The output ray can be focused on a fixed photodetector by a convex lens.Thus it can be avoided that a prism and a photodetector rotate by θ and 2θ respectively in conventional angular scanning SPR sensor.This new design reduces the number of the movable components,makes the structure simple and compact,and makes the manipulation convenient.

  7. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

    Science.gov (United States)

    Zhang, Qiong; Maldague, Xavier

    2016-01-01

    A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images. Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s). In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm. Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.

  8. Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Fafoutis, Xenofon; Dragoni, Nicola

    2012-01-01

    ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever......-changing environmental energy sources. In this paper, we present an improved and extended version of ODMAC and we analyze it by means of an analytical model that can approximate several performance metrics in an arbitrary network topology. The simulations and the analytical experiments show ODMAC's ability to satisfy...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....

  9. Image-Based Multi-Sensor Data Representation and Fusion Via 2D Non-Linear Convolution

    OpenAIRE

    Aaron R. Rababaah

    2012-01-01

    Sensor data fusion is the process of combining data collected from multi sensors of homogeneous or heterogeneous modalities to perform inferences that may not be possible using a single sensor. This process encompasses several stages to arrive at a sound reliable decision making end result. These stages include: senor-signal preprocessing, sub-object refinement, object refinement, situation refinement, threat refinement and process refinement. Every stage draws from different domains to achie...

  10. Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

    Institute of Scientific and Technical Information of China (English)

    Xiao-Lin Li; Jian-Nong Cao

    2008-01-01

    To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present acoordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node,in-network data aggregation is further considered. The scheduling strategies consist of two phases: intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

  11. Sensor fusion methods for reducing false alarms in heart rate monitoring.

    Science.gov (United States)

    Borges, Gabriel; Brusamarello, Valner

    2016-12-01

    Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

  12. Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

    Directory of Open Access Journals (Sweden)

    Zhenghua Chen

    2015-01-01

    Full Text Available Location-based services (LBS have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

  13. Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization.

    Science.gov (United States)

    Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

    2015-01-05

    Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

  14. Adaptive polarization image fusion based on regional energy dynamic weighted average

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yong-qiang; PAN Quan; ZHANG Hong-cai

    2005-01-01

    According to the principle of polarization imaging and the relation between Stokes parameters and the degree of linear polarization, there are much redundant and complementary information in polarized images. Since man-made objects and natural objects can be easily distinguished in images of degree of linear polarization and images of Stokes parameters contain rich detailed information of the scene, the clutters in the images can be removed efficiently while the detailed information can be maintained by combining these images. An algorithm of adaptive polarization image fusion based on regional energy dynamic weighted average is proposed in this paper to combine these images. Through an experiment and simulations,most clutters are removed by this algorithm. The fusion method is used for different light conditions in simulation, and the influence of lighting conditions on the fusion results is analyzed.

  15. Adaptive Multichannel Radiation Sensors for Plant Parameter Monitoring

    Science.gov (United States)

    Mollenhauer, Hannes; Remmler, Paul; Schuhmann, Gudrun; Lausch, Angela; Merbach, Ines; Assing, Martin; Mollenhauer, Olaf; Dietrich, Peter; Bumberger, Jan

    2016-04-01

    Nutrients such as nitrogen are playing a key role in the plant life cycle. They are much needed for chlorophyll production and other plant cell components. Therefore, the crop yield is strongly affected by plant nutrient status. Due to the spatial and temporal variability of soil characteristics or swaying agricultural inputs the plant development varies within a field. Thus, the determination of these fluctuations in the plant development is valuable for a detection of stress conditions and optimization of fertilisation due to its high environmental and economic impact. Plant parameters play crucial roles in plant growth estimation and prediction since they are used as indicators of plant performance. Especially indices derived out of remote sensing techniques provide quantitative information about agricultural crops instantaneously, and above all, non-destructively. Due to the specific absorption of certain plant pigments, a characteristic spectral signature can be seen in the visible and IR part of the electromagnetic spectrum, known as narrow-band peaks. In an analogous manner, the presence and concentration of different nutrients cause a characteristic spectral signature. To this end, an adequate remote sensing monitoring concept is needed, considering heterogeneity and dynamic of the plant population and economical aspects. This work will present the development and field investigations of an inexpensive multichannel radiation sensor to observe the incoming and reflected specific parts or rather distinct wavelengths of the solar light spectrum on the crop and facilitate the determination of different plant indices. Based on the selected sensor wavelengths, the sensing device allows the detection of specific parameters, e.g. plant vitality, chlorophyll content or nitrogen content. Besides the improvement of the sensor characteristic, the simple wavelength adaption, and the price-performance ratio, the achievement of appropriate energy efficiency as well as a

  16. On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

    Science.gov (United States)

    Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2012-01-01

    The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

  17. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Ignacio Rojas

    2012-06-01

    Full Text Available The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

  18. Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

    Science.gov (United States)

    Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

    2010-01-01

    The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

  19. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

    Directory of Open Access Journals (Sweden)

    Arturo de la Escalera

    2010-08-01

    Full Text Available The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem and dense disparity maps and u-v disparity (vision subsystem. Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

  20. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    Science.gov (United States)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  1. The addition of a sagittal image fusion improves the prostate cancer detection in a sensor-based MRI /ultrasound fusion guided targeted biopsy.

    Science.gov (United States)

    Günzel, Karsten; Cash, Hannes; Buckendahl, John; Königbauer, Maximilian; Asbach, Patrick; Haas, Matthias; Neymeyer, Jörg; Hinz, Stefan; Miller, Kurt; Kempkensteffen, Carsten

    2017-01-13

    To explore the diagnostic benefit of an additional image fusion of the sagittal plane in addition to the standard axial image fusion, using a sensor-based MRI/US fusion platform. During July 2013 and September 2015, 251 patients with at least one suspicious lesion on mpMRI (rated by PI-RADS) were included into the analysis. All patients underwent MRI/US targeted biopsy (TB) in combination with a 10 core systematic prostate biopsy (SB). All biopsies were performed on a sensor-based fusion system. Group A included 162 men who received TB by an axial MRI/US image fusion. Group B comprised 89 men in whom the TB was performed with an additional sagittal image fusion. The median age in group A was 67 years (IQR 61-72) and in group B 68 years (IQR 60-71). The median PSA level in group A was 8.10 ng/ml (IQR 6.05-14) and in group B 8.59 ng/ml (IQR 5.65-12.32). In group A the proportion of patients with a suspicious digital rectal examination (DRE) (14 vs. 29%, p = 0.007) and the proportion of primary biopsies (33 vs 46%, p = 0.046) were significantly lower. The rate of PI-RADS 3 lesions were overrepresented in group A compared to group B (19 vs. 9%; p = 0.044). Classified according to PI-RADS 3, 4 and 5, the detection rates of TB were 42, 48, 75% in group A and 25, 74, 90% in group B. The rate of PCa with a Gleason score ≥7 missed by TB was 33% (18 cases) in group A and 9% (5 cases) in group B; p-value 0.072. An explorative multivariate binary logistic regression analysis revealed that PI-RADS, a suspicious DRE and performing an additional sagittal image fusion were significant predictors for PCa detection in TB. 9 PCa were only detected by TB with sagittal fusion (sTB) and sTB identified 10 additional clinically significant PCa (Gleason ≥7). Performing an additional sagittal image fusion besides the standard axial fusion appears to improve the accuracy of the sensor-based MRI/US fusion platform.

  2. Fusion of ground-penetrating radar and electromagnetic induction sensors for landmine detection and discrimination

    Science.gov (United States)

    Kolba, Mark P.; Torrione, Peter A.; Collins, Leslie M.

    2010-04-01

    Ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors provide complementary capabilities in detecting buried targets such as landmines, suggesting that the fusion of GPR and EMI modalities may provide improved detection performance over that obtained using only a single modality. This paper considers both pre-screening and the discrimination of landmines from non-landmine objects using real landmine data collected from a U.S. government test site as part of the Autonomous Mine Detection System (AMDS) landmine program. GPR and EMI pre-screeners are first reviewed and then a fusion pre-screener is presented that combines the GPR and EMI prescreeners using a distance-based likelihood ratio test (DLRT) classifier to produce a fused confidence for each pre-screener alarm. The fused pre-screener is demonstrated to provide substantially improved performance over the individual GPR and EMI pre-screeners. The discrimination of landmines from non-landmine objects using feature-based classifiers is also considered. The GPR feature utilized is a pre-processed, spatially filtered normalized energy metric. Features used for the EMI sensor include model-based features generated from the AETC model and a dipole model as well as features from a matched subspace detector. The EMI and GPR features are then fused using a random forest classifier. The fused classifier performance is superior to the performance of classifiers using GPR or EMI features alone, again indicating that performance improvements may be obtained through the fusion of GPR and EMI sensors. The performance improvements obtained both for pre-screening and for discrimination have been verified by blind test results scored by an independent U.S. government contractor.

  3. Coal blending scheduling in coal preparation plant based on multi-sensor information fusion

    Energy Technology Data Exchange (ETDEWEB)

    Gao, L.; Yu, H.; Wang, Y. [CUMT, Xuzhou (China). School of Information and Electrical Engineering

    2004-01-01

    It is important to research on a reasonable blending schedule according to the customer requirement and the practice of products in coal preparation plant. In order to solve this problem, a mathematic model was set up on the basis of analysing coal blending schedule. Multi-sensors information fusion was used to monitor the density and the amount of coal. The genetic algorithm was used to solve the nonlinear function in the maths model. A satisfied result was obtained by simulating test. 8 refs., 2 figs.

  4. Optical Communication System for Remote Monitoring and Adaptive Control of Distributed Ground Sensors Exhibiting Collective Intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Cameron, S.M.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-11-01

    Comprehensive management of the battle-space has created new requirements in information management, communication, and interoperability as they effect surveillance and situational awareness. The objective of this proposal is to expand intelligent controls theory to produce a uniquely powerful implementation of distributed ground-based measurement incorporating both local collective behavior, and interoperative global optimization for sensor fusion and mission oversight. By using a layered hierarchal control architecture to orchestrate adaptive reconfiguration of autonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecks. In this concept, each sensor is equipped with a miniaturized optical reflectance modulator which is interactively monitored as a remote transponder using a covert laser communication protocol from a remote mothership or operative. Robot data-sharing at the ground level can be leveraged with global evaluation criteria, including terrain overlays and remote imaging data. Information sharing and distributed intelli- gence opens up a new class of remote-sensing applications in which small single-function autono- mous observers at the local level can collectively optimize and measure large scale ground-level signals. AS the need for coverage and the number of agents grows to improve spatial resolution, cooperative behavior orchestrated by a global situational awareness umbrella will be an essential ingredient to offset increasing bandwidth requirements within the net. A system of the type described in this proposal will be capable of sensitively detecting, tracking, and mapping spatial distributions of measurement signatures which are non-stationary or obscured by clutter and inter- fering obstacles by virtue of adaptive reconfiguration. This methodology could be used, for example, to field an adaptive ground-penetrating radar for detection of underground structures in

  5. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

    Science.gov (United States)

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-07-19

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  6. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

  7. Adaptive Multi-Modal Data Mining and Fusion for Autonomous Intelligence Discovery

    Science.gov (United States)

    2009-03-01

    Final DATES COVERED (From To) From 15-12-2006 to 15-12-2007 4. TITLE AND SUBTITLE Adaptive Multi-Modal Data Mining and Fusion For Autonomous...well as geospatial mapping of documents and images. 15. SUBJECT TERMS automated data mining , streaming data, geospatial Internet localization, Arabic...streaming text data mining . 1.1 Mixed Language Text Database Search A particularly useful component that was under development was on a mixed language

  8. Reconnaissance blind multi-chess: an experimentation platform for ISR sensor fusion and resource management

    Science.gov (United States)

    Newman, Andrew J.; Richardson, Casey L.; Kain, Sean M.; Stankiewicz, Paul G.; Guseman, Paul R.; Schreurs, Blake A.; Dunne, Jeffrey A.

    2016-05-01

    This paper introduces the game of reconnaissance blind multi-chess (RBMC) as a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of heterogeneous Intelligence, Surveillance and Reconnaissance (ISR) sensors to maintain situational awareness informing tactical and strategic decision making. The intent is for RBMC to serve as a common reference or challenge problem in fusion and resource management of heterogeneous sensor ensembles across diverse mission areas. We have defined a basic rule set and a framework for creating more complex versions, developed a web-based software realization to serve as an experimentation platform, and developed some initial machine intelligence approaches to playing it.

  9. Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

    Science.gov (United States)

    Mishra, Spandan; Vanli, O. Arda; Huffer, Fred W.; Jung, Sungmoon

    2016-04-01

    In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

  10. Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots

    Science.gov (United States)

    Emter, Thomas; Petereit, Janko

    2014-05-01

    An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

  11. An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation.

    Science.gov (United States)

    He, Changyu; Kazanzides, Peter; Sen, Hasan Tutkun; Kim, Sungmin; Liu, Yue

    2015-07-08

    Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions.

  12. An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation

    Directory of Open Access Journals (Sweden)

    Changyu He

    2015-07-01

    Full Text Available Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD that integrates an optical tracking system (OTS and inertial measurement unit (IMU. Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions.

  13. An adaptive technique for a redundant-sensor navigation system.

    Science.gov (United States)

    Chien, T.-T.

    1972-01-01

    An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. This adaptive system is structured as a multistage stochastic process of detection, identification, and compensation. It is shown that the detection system can be effectively constructed on the basis of a design value, specified by mission requirements, of the unknown parameter in the actual system, and of a degradation mode in the form of a constant bias jump. A suboptimal detection system on the basis of Wald's sequential analysis is developed using the concept of information value and information feedback. The developed system is easily implemented, and demonstrates a performance remarkably close to that of the optimal nonlinear detection system. An invariant transformation is derived to eliminate the effect of nuisance parameters such that the ambiguous identification system can be reduced to a set of disjoint simple hypotheses tests. By application of a technique of decoupled bias estimation in the compensation system the adaptive system can be operated without any complicated reorganization.

  14. Sensor fusion: lane marking detection and autonomous intelligent cruise control system

    Science.gov (United States)

    Baret, Marc; Baillarin, S.; Calesse, C.; Martin, Lionel

    1995-12-01

    In the past few years MATRA and RENAULT have developed an Autonomous Intelligent Cruise Control (AICC) system based on a LIDAR sensor. This sensor incorporating a charge coupled device was designed to acquire pulsed laser diode emission reflected by standard car reflectors. The absence of moving mechanical parts, the large field of view, the high measurement rate and the very good accuracy for distance range and angular position of targets make this sensor very interesting. It provides the equipped car with the distance and the relative speed of other vehicles enabling the safety distance to be controlled by acting on the throttle and the automatic gear box. Experiments in various real traffic situations have shown the limitations of this kind of system especially on bends. All AICC sensors are unable to distinguish between a bend and a change of lane. This is easily understood if we consider a road without lane markings. This fact has led MATRA to improve its AICC system by providing the lane marking information. Also in the scope of the EUREKA PROMETHEUS project, MATRA and RENAULT have developed a lane keeping system in order to warn of the drivers lack of vigilance. Thus, MATRA have spread this system to far field lane marking detection and have coupled it with the AICC system. Experiments will be carried out on roads to estimate the gain in performance and comfort due to this fusion.

  15. Fault tolerant multi-sensor fusion based on the information gain

    Science.gov (United States)

    Hage, Joelle Al; El Najjar, Maan E.; Pomorski, Denis

    2017-01-01

    In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.

  16. Fusion

    Science.gov (United States)

    Herman, Robin

    1990-10-01

    The book abounds with fascinating anecdotes about fusion's rocky path: the spurious claim by Argentine dictator Juan Peron in 1951 that his country had built a working fusion reactor, the rush by the United States to drop secrecy and publicize its fusion work as a propaganda offensive after the Russian success with Sputnik; the fortune Penthouse magazine publisher Bob Guccione sank into an unconventional fusion device, the skepticism that met an assertion by two University of Utah chemists in 1989 that they had created "cold fusion" in a bottle. Aimed at a general audience, the book describes the scientific basis of controlled fusion--the fusing of atomic nuclei, under conditions hotter than the sun, to release energy. Using personal recollections of scientists involved, it traces the history of this little-known international race that began during the Cold War in secret laboratories in the United States, Great Britain and the Soviet Union, and evolved into an astonishingly open collaboration between East and West.

  17. A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    In order to enhance the image information from multi-sensor and to improve the abilities of theinformation analysis and the feature extraction, this letter proposed a new fusion approach in pixel level bymeans of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequencyband and high frequency band in higher scale. It offers a more precise method for image analysis than Wave-let Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform toobtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. ThenWPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observationde la Therre ) image into low frequency band and high frequency band in three levels. Next, two high fre-quency coefficients and low frequency coefficients of the images are combined by linear weighting strategies.Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approachcan fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM(Histogram Matched)-based fusion algorithm and WT-based fusion approach.

  18. Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver

    Directory of Open Access Journals (Sweden)

    Chan-Gun Lee

    2016-06-01

    Full Text Available Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE before and after applying our mechanism are significantly reduced from 6.3 × 10−1 to 5.3 × 10−7, respectively.

  19. Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

    Science.gov (United States)

    Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

    2017-01-01

    The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

  20. Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

    Science.gov (United States)

    Lee, Chan-Gun; Dao, Nhu-Ngoc; Jang, Seonmin; Kim, Deokhwan; Kim, Yonghun; Cho, Sungrae

    2016-06-11

    Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10(-1) to 5.3 × 10(-7), respectively.

  1. Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.

    Science.gov (United States)

    Chen, Zhenguo; Tian, Liqin; Lin, Chuang

    2017-03-28

    In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.

  2. Adaptive and controllable compliant systems with embedded actuators and sensors

    Science.gov (United States)

    Trease, Brian; Kota, Sridhar

    2007-04-01

    We present a framework for the design of a compliant system; i.e. the concurrent design of a compliant mechanism with embedded actuators and embedded sensors. Our methods simultaneously synthesize optimal structural topology and placement of actuators and sensors for maximum energy efficiency and adaptive performance, while satisfying various weight and performance constraints. The goal of this research is to lay an algorithmic framework for distributed actuation and sensing within a compliant active structure. Key features of the methodology include (1) the simultaneous optimization of the location, orientation, and size of actuators concurrent with the compliant transmission topology and (2) the concepts of controllability and observability that arise from the consideration of control, and their implementation in compliant systems design. The methods used include genetic algorithms, graph searches for connectivity, and multiple load cases implemented with linear finite element analysis. Actuators, modeled as both force generators and structural compliant elements, are included as topology variables in the optimization. Results are provided for several studies, including: (1) concurrent actuator placement and topology design for a compliant amplifier and (2) a shape-morphing aircraft wing demonstration with three controlled output nodes. Central to this method is the concept of structural orthogonality, which refers to the unique system response for each actuator it contains. Finally, the results from the controllability problem are used to motivate and describe the analogous extension to observability for sensing.

  3. 基于自适应数据融合的LEACH路由协议%LEACH based on adaptive data fusion

    Institute of Scientific and Technical Information of China (English)

    王培东; 袁召兰; 王瑜

    2011-01-01

    How to effectively manage power consumption so that the lifetime of a WSN can be maximized is a important goal for routing protocol design in wireless sensor networks. Based on LEACH, we proposed a new routing protocol AF-LEACH(Adaptive Fusion LEACH ),which can adaptively adjust whether data fusion gathered in sensor nodes shall be performed according to the cost and benefit of the data fusion. The simulation results demonstrate that AF-LEACH is more effective than LEACH and MA-LEACH in which data fusion gathered in each node shall be performed in the aspects of reducing node energy consumption and extending the network lifetime.%如何有效地使用传感器节点的能量以延长WSN的生存时间,一直是WSN路由协议研究的重点.基于LEACH,提出了一种新的路由协议AF-LEACH,AF-LEACH根据数据融合的能量开销和所带来的节能增益,对传感器节点采集的数据进行自适应的数据融合.仿真实验表明,与LEACH协议以及在各节点都进行数据融合的MA-LEACH[1]协议相比,AF-LEACH在降低节点能耗,延长网络寿命等方面上有了显著提高.

  4. A robust data fusion scheme for integrated navigation systems employing fault detection methodology augmented with fuzzy adaptive filtering

    Science.gov (United States)

    Ushaq, Muhammad; Fang, Jiancheng

    2013-10-01

    Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be

  5. AUVs as integrated, adaptive acoustic sensors for ocean exploration

    Science.gov (United States)

    Schmidt, Henrik; Edwards, Joseph R.; Liu, Te-Chih; Montanari, Monica

    2001-05-01

    Autonomous underwater vehicles (AUV) are rapidly being transitioned into operational systems for national defense, offshore exploration, and ocean science. AUVs provide excellent sensor platform control, allowing for, e.g., accurate acoustic mapping of seabeds not easily reached by conventional platforms, such as the deep ocean. However, the full potential of the robotic platforms is far from exhausted by such applications. Thus, for example, most seabed-mapping applications use imaging sonar technology, the data volume of which cannot be transmitted back to the operators in real time due to the severe bandwidth limitation of the acoustic communication. The sampling patterns are therefore in general being preprogramed and the data are being stored for postmission analysis. This procedure is therefore associated with indiscriminate distribution of the sampling throughout the area of interest, irrespective of whether features of interest are present or not. However, today's computing technology allows for a significant amount of signal processing and analysis to be performed on the platforms, where the results may then be used for real-time adaptive sampling to optimally concentrate the sampling in area of interest, and compress the results to a few parameters which may be transmitted back to the operators. Such adaptive sensing concepts combining environmental acoustics, signal processing, and robotics are currently being developed for concurrent detection, localization, and classification of buried objects, with application to littoral mine countermeasures, deep ocean seabed characterization, and marine archeology. [Work supported by ONR and NATO Undersea Research Center.

  6. Pose estimation of surgical instrument using sensor data fusion with optical tracker and IMU based on Kalman filter

    Directory of Open Access Journals (Sweden)

    Oh Hyunmin

    2015-01-01

    Full Text Available Tracking system is essential for Image Guided Surgery(IGS. The Optical Tracking Sensor(OTS has been widely used as tracking system for IGS due to its high accuracy and easy usage. However, OTS has a limit that tracking fails when occlusion of marker occurs. In this paper, sensor fusion with OTS and Inertial Measurement Unit(IMU is proposed to solve this problem. The proposed algorithm improves the accuracy of tracking system by eliminating scattering error of the sensor and supplements the disadvantages of OTS and IMU through sensor fusion based on Kalman filter. Also, coordinate axis calibration method that improves the accuracy is introduced. The performed experiment verifies the effectualness of the proposed algorithm.

  7. Temporal Pattern Recognition: A Network Architecture For Multi-Sensor Fusion

    Science.gov (United States)

    Priebe, C. E.; Marchette, D. J.

    1989-03-01

    A self-organizing network architecture for the learning and recognition of temporal patterns is proposed. This multi-layered architecture has as its focal point a layer of multi-dimensional Gaussian classification nodes, and the learning scheme employed is based on standard statistical moving mean and moving covariance calculations. The nodes are implemented in the network architecture by using a Gaussian, rather than sigmoidal, transfer function acting on the input from numerous connections. Each connection is analogous to a separate dimension for the Gaussian function. The learning scheme is a one-pass method, eliminating the need for repetitive presentation of the teaching stimuli. The Gaussian classes developed are representative of the statistics of the teaching data and act as templates in classifying novel inputs. The input layer employs a time-based decay to develop a time-ordered representation of the input stimuli. This temporal pattern recognition architecture is used to perform multi-sensor fusion and scene analysis for ROBART II, an autonomous sentry robot employing heterogeneous and homogeneous binary (on / off) sensors. The system receives sensor packets from ROBART indicating which sensors are active. The packets from various sensors are integrated in the input layer. As time progresses these sensor outputs become ordered, allowing the system to recognize activities which are dependent, not only on the individual events which make up the activity, but also on the order in which these events occur and their relative spacing throughout time. Each Gaussian classification node, representing a learned activity as an ordered sequence of sensor outputs, calculates its activation value independently, based on the activity in the input layer. These Gaussian activation values are then used to determine which, if any, of the learned sequences are present and with what confidence. The classification system is capable of recognizing activities despite missing

  8. Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots.

    Science.gov (United States)

    Marín, Leonardo; Vallés, Marina; Soriano, Ángel; Valera, Ángel; Albertos, Pedro

    2013-10-21

    This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.

  9. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

    Directory of Open Access Journals (Sweden)

    Ningbo Yu

    2016-03-01

    Full Text Available Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs, and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.

  10. Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

    Science.gov (United States)

    Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

    2014-05-01

    With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

  11. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation.

    Science.gov (United States)

    Yu, Ningbo; Xu, Chang; Li, Huanshuai; Wang, Kui; Wang, Liancheng; Liu, Jingtai

    2016-03-18

    Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.

  12. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

    Science.gov (United States)

    Yu, Ningbo; Xu, Chang; Li, Huanshuai; Wang, Kui; Wang, Liancheng; Liu, Jingtai

    2016-01-01

    Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training. PMID:26999149

  13. A weighted optimization approach to time-of-flight sensor fusion.

    Science.gov (United States)

    Schwarz, Sebastian; Sjostrom, Marten; Olsson, Roger

    2014-01-01

    Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

  14. Multi Sensor Fusion Framework for Indoor-Outdoor Localization of Limited Resource Mobile Robots

    Directory of Open Access Journals (Sweden)

    Pedro Albertos

    2013-10-01

    Full Text Available This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU on an event based schedule, using fewer resources (execution time and bandwidth but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.

  15. Classification of paddy rice through multi-temporal multi-sensor data fusion

    Science.gov (United States)

    Im, Jungho; Park, Seonyoung

    2017-04-01

    Rice is one of important food resources in the world and its consumption continues to increase with increasing world population. Accurate paddy rice mapping and monitoring are crucial for food security and agricultural mitigation because they enable us to forecast rice production. There have been studies for paddy rice classification using optical sensor data. However, optical sensor data has a limitation for data acquisition due to cloud contamination. Active Synthetic Aperture Radar (SAR) data have been used to complement the cloud problems of optical sensor images. Integration of the multispectral and SAR data can produce the more reliable crop classification results than from a single sensor data. In addition, as paddy rice has distinct phenology, many studies used phenology features from multi-temporal data for detecting paddy rice. Thus, this study aims at mapping paddy rice by expanding the spectral and temporal dimensions of data. In this study, we conducted paddy rice classification through fusion of multi-temporal optical sensor (Landsat) and SAR (RADARSAT-1 and ALSO PALSAR) data using two machine learning approaches—random forest (RF) and support vector machines (SVM) over two study sites (Dangjin-si in South Korea and Sutter County, California in the United States). This study examined six scenarios to identify the effect of the expansion of data dimension. Each scenario has a different combination of data sources and seasonal characteristics. We examined variable importance to identify which sensor data collected at which season are important to classify paddy rice. In addition, this study proposed a new index called Paddy rice Mapping Index (PMI) for effective paddy rice classification considering the spectral and temporal characteristics of paddy rice. Scenario 6 that uses optical sensor and SAR multi temporal data showed the highest overall accuracy (site 1: 98.67%; site 2: 93.87%) for paddy rice classification among six scenarios. Both machine

  16. Geometric calibration of multi-sensor image fusion system with thermal infrared and low-light camera

    Science.gov (United States)

    Peric, Dragana; Lukic, Vojislav; Spanovic, Milana; Sekulic, Radmila; Kocic, Jelena

    2014-10-01

    A calibration platform for geometric calibration of multi-sensor image fusion system is presented in this paper. The accurate geometric calibration of the extrinsic geometric parameters of cameras that uses planar calibration pattern is applied. For calibration procedure specific software is made. Patterns used in geometric calibration are prepared with aim to obtain maximum contrast in both visible and infrared spectral range - using chessboards which fields are made of different emissivity materials. Experiments were held in both indoor and outdoor scenarios. Important results of geometric calibration for multi-sensor image fusion system are extrinsic parameters in form of homography matrices used for homography transformation of the object plane to the image plane. For each camera a corresponding homography matrix is calculated. These matrices can be used for image registration of images from thermal and low light camera. We implemented such image registration algorithm to confirm accuracy of geometric calibration procedure in multi-sensor image fusion system. Results are given for selected patterns - chessboard with fields made of different emissivity materials. For the final image registration algorithm in surveillance system for object tracking we have chosen multi-resolution image registration algorithm which naturally combines with a pyramidal fusion scheme. The image pyramids which are generated at each time step of image registration algorithm may be reused at the fusion stage so that overall number of calculations that must be performed is greatly reduced.

  17. Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

    Science.gov (United States)

    Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang

    2011-08-01

    Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover

  18. Path Planning Algorithms for the Adaptive Sensor Fleet

    Science.gov (United States)

    Stoneking, Eric; Hosler, Jeff

    2005-01-01

    The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

  19. A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

    Science.gov (United States)

    Tannous, Halim; Istrate, Dan; Benlarbi-Delai, Aziz; Sarrazin, Julien; Gamet, Didier; Ho Ba Tho, Marie Christine; Dao, Tien Tuan

    2016-01-01

    Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation. PMID:27854288

  20. Real-Time Classification and Sensor Fusion with a Spiking Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Peter eO'Connor

    2013-10-01

    Full Text Available Deep Belief Networks (DBNs have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128x128 Dynamic Vision Sensor (DVS silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.

  1. ADS-B and multilateration sensor fusion algorithm for air traffic control

    Science.gov (United States)

    Liang, Mengchen

    Air traffic is expected to increase rapidly in the next decade. But, the current Air Traffic Control (ATC) system does not meet the demand of the future safety and efficiency. The Next Generation Air Transportation System (NextGen) is a transformation program for the ATC system in the United States. The latest estimates by Federal Aviation Administration (FAA) show that by 2018 NextGen will reduce total delays in flight by 35 percent and provide 23 billion dollars in cumulative benefits. A satellite-based technology called the Automatic Dependent Surveillance-Broadcast (ADS-B) system is one of the most important elements in NextGen. FAA expects that ADS-B systems will be available in the National Airspace System (NAS) by 2020. However, an alternative surveillance system is needed due to vulnerabilities that exist in ADS-B systems. Multilateration has a high accuracy performance and is believed to be an ideal back-up strategy for ADS-B systems. Thus, in this study, we develop the ADS-B and multilateration sensor fusion algorithm for aircraft tracking applications in ATC. The algorithm contains a fault detection function for ADS-B information monitoring by using Trajectory Change Points reports from ADS-B and numerical vectors from a hybrid estimation algorithm. We consider two types of faults in the ADS-B measurement model to show that the algorithm is able to deal with the bad data from ADS-B systems and automatically select good data from multilateration systems. We apply fuzzy logic concepts and generate time variant parameters during the fusion process. The parameters play a role of weights for combining data from different sensors. The algorithm performance is validated through two aircraft tracking examples.

  2. Multiple sensor detection of process phenomena in laser powder bed fusion

    Science.gov (United States)

    Lane, Brandon; Whitenton, Eric; Moylan, Shawn

    2016-05-01

    Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process in which a high power laser melts metal powder layers into complex, three-dimensional shapes. LPBF parts are known to exhibit relatively high residual stresses, anisotropic microstructure, and a variety of defects. To mitigate these issues, in-situ measurements of the melt-pool phenomena may illustrate relationships between part quality and process signatures. However, phenomena such as spatter, plume formation, laser modulation, and melt-pool oscillations may require data acquisition rates exceeding 10 kHz. This hinders use of relatively data-intensive, streaming imaging sensors in a real-time monitoring and feedback control system. Single-point sensors such as photodiodes provide the temporal bandwidth to capture process signatures, while providing little spatial information. This paper presents results from experiments conducted on a commercial LPBF machine which incorporated synchronized, in-situ acquisition of a thermal camera, high-speed visible camera, photodiode, and laser modulation signal during fabrication of a nickel alloy 625 AM part with an overhang geometry. Data from the thermal camera provides temperature information, the visible camera provides observation of spatter, and the photodiode signal provides high temporal bandwidth relative brightness stemming from the melt pool region. In addition, joint-time frequency analysis (JTFA) was performed on the photodiode signal. JTFA results indicate what digital filtering and signal processing are required to highlight particular signatures. Image fusion of the synchronized data obtained over multiple build layers allows visual comparison between the photodiode signal and relating phenomena observed in the imaging detectors.

  3. Real-time classification and sensor fusion with a spiking deep belief network

    Science.gov (United States)

    O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

    2013-01-01

    Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input. PMID:24115919

  4. Pose estimation of surgical instrument using sensor data fusion with optical tracker and IMU based on Kalman filter

    OpenAIRE

    Oh Hyunmin; Chae You Seong; An Jinung; Kim Min Young

    2015-01-01

    Tracking system is essential for Image Guided Surgery(IGS). The Optical Tracking Sensor(OTS) has been widely used as tracking system for IGS due to its high accuracy and easy usage. However, OTS has a limit that tracking fails when occlusion of marker occurs. In this paper, sensor fusion with OTS and Inertial Measurement Unit(IMU) is proposed to solve this problem. The proposed algorithm improves the accuracy of tracking system by eliminating scattering error of the sensor and supplements the...

  5. Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

    Science.gov (United States)

    Leptoukh, Gregory; Zubko, V.; Gopalan, A.

    2007-01-01

    The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

  6. Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion.

    Science.gov (United States)

    Ljungblad, Jonas; Hök, Bertil; Allalou, Amin; Pettersson, Håkan

    2017-04-03

    The research objective of the present investigation is to demonstrate the present status of passive in-vehicle driver breath alcohol detection and highlighting the necessary conditions for large scale implementation of such a system. Completely passive detection has remained a challenge mainly because of the requirements on signal resolution combined with the constraints of vehicle integration. The work is part of the DADSS (driver alcohol detection system for safety) program aiming at massive deployment of alcohol sensing systems which could potentially save thousands of American lives annually. The work reported here builds on earlier investigations, in which it has been shown that detection of alcohol vapor in the proximity of a human subject may be traced to that subject by means of simultaneous recording of carbon dioxide (CO2) at the same location. Sensors based on infrared spectroscopy were developed to detect and quantify low concentrations of alcohol and CO2. In the present investigation, alcohol and CO2 were recorded at various locations in a vehicle cabin while human subjects were performing normal in-step procedures and driving preparations. A video camera directed to the driver position was recording images of the driver's upper body parts including the face, and the images were analyzed with respect to features of significance to the breathing behavior and breath detection, such as mouth opening and head direction. Improvement of the sensor system with respect to signal resolution including algorithm and software development, and fusion of the sensor and camera signals was successfully implemented and tested before starting the human study. In addition, experimental tests and simulations were performed with the purpose of connecting human subject data with repeatable experimental conditions. The results include occurrence statistics of detected breaths by signal peaks of CO2 and alcohol. From the statistical data, the accuracy of breath alcohol

  7. Aspects of sensor data fusion in interoperable ISR systems of systems for wide-area ground surveillance

    Science.gov (United States)

    Koch, Wolfgang; Ulmke, Martin; Biermann, Joachim; Sielemann, Marion

    2010-04-01

    Within the context of C4ISTAR information "systems of systems", we discuss sensor data fusion aspects that are aiming at the generation of higher-level in-formation according to the JDL model of data fusion. In particular, two issues are addressed: (1) Tracking-derived Situation Elements: Standard target tracking applications gain information related to 'Level 1 Fusion' according to the well-established terminology of the JDL model. Kinematic data of this type, however, are by no means the only information to be derived from tar-get tracks. In many cases, reliable and quantitative higher level information according to the JDL terminology can be obtained. (2) Anomaly Detection in Tracking Data Bases: Anomaly detection can be regarded as a process of information fusion that aims at focusing the attention of human decision makers or decision making systems is focused on particular events that are "irregular" or may cause harm and thus require special actions.

  8. THE ADAPTIVE SMOOTHING FILTERS OF SENSOR SIGNALS IN THE MICROAVIONIC SYSTEMS

    Directory of Open Access Journals (Sweden)

    V. A. Malkin

    2012-01-01

    Full Text Available The adaptive for intensivity of measuring noise filters for smooth of sensor signals are considered. The adaptation are realized at the expense of the statistical processing of the filtering errors. The algorithm of adaptive filter coefficients calculation and modeling results are presented.

  9. Particle filter based visual tracking with multi-cue adaptive fusion

    Institute of Scientific and Technical Information of China (English)

    Anping Li; Zhongliang Jing; Shiqiang Hu

    2005-01-01

    @@ To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.

  10. The application of machine learning in multi sensor data fusion for activity recognition in mobile device space

    Science.gov (United States)

    Marhoubi, Asmaa H.; Saravi, Sara; Edirisinghe, Eran A.

    2015-05-01

    The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device's behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.

  11. Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking

    Directory of Open Access Journals (Sweden)

    Filiz Bunyak

    2007-08-01

    Full Text Available This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding ”hot-spots”, and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shapebased model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations.

  12. Sensor Fusion of Cameras and a Laser for City-Scale 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    Yunsu Bok

    2014-11-01

    Full Text Available This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

  13. Sensor fusion of cameras and a laser for city-scale 3D reconstruction.

    Science.gov (United States)

    Bok, Yunsu; Choi, Dong-Geol; Kweon, In So

    2014-11-04

    This paper presents a sensor fusion system of cameras and a 2D laser sensorfor large-scale 3D reconstruction. The proposed system is designed to capture data on afast-moving ground vehicle. The system consists of six cameras and one 2D laser sensor,and they are synchronized by a hardware trigger. Reconstruction of 3D structures is doneby estimating frame-by-frame motion and accumulating vertical laser scans, as in previousworks. However, our approach does not assume near 2D motion, but estimates free motion(including absolute scale) in 3D space using both laser data and image features. In orderto avoid the degeneration associated with typical three-point algorithms, we present a newalgorithm that selects 3D points from two frames captured by multiple cameras. The problemof error accumulation is solved by loop closing, not by GPS. The experimental resultsshow that the estimated path is successfully overlaid on the satellite images, such that thereconstruction result is very accurate.

  14. Pixel-by-pixel VIS/NIR and LIR sensor fusion system

    Science.gov (United States)

    Zhang, Evan; Zhang, James S.; Song, Vivian W.; Chin, Ken P.; Hu, Gelbert

    2003-01-01

    Visible (VIS) camera (such as CCD) or Near Infrared (NIR) camera (such as low light level CCD or image intensifier) has high resolution and is easy to distinguish enemy and foe, but it cannot see through thin fog/cloud, heavy smoke/dust, foliage, camouflage, and darkness. The Long Infrared (LIR) imager can overcome above problems, but the resolution is too low and it cannot see the NIR aiming light from enemy. The best solution is to fuse the VIS/NIR and LIR sensors to overcome their shortcomings and take advantages of both sensors. In order to see the same target without parallax, the fusio system must have a common optical aperature. In this paper, three common optical apertures are designed: common reflective objective lens, common beam splitter, and common transmissive objective lens. The first one has very small field of view and the second one needs two heads, so the best choice is the third one, but we must find suitable optical materials and correct the color aberrations from 0.6 to 12 μ. It is a tough job. By choosing ZnSe as the first common piece of the objective lens and using glass for NIR and Ge (or IR glass) for LIR as rest pieces, we only need to and are able to correct the aberrations from 0.6 to 1.0 μ for NIR and from 8 to 12 μ for LIR. Finally, a common reflective objective lens and the common beam splitter are also successfully designed. Five application examples are given. In the digital signal processing, we use only one Altera chip. After inserting data, scaling the image size, and adjusting the signal level, the LIR will have the same format and same pixel number of the VIS/NIR, so real-time pixel-by-pixel sensor fusion is realized. The digital output can be used for further image processing and automatic target recognition, such as if we overlap the LIR image on the VIS/NIR image for missile guidance or rifle sight we don't need to worry about the time and the environment again. A gum-size wireless transmitter is also designed that is

  15. Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors.

    Science.gov (United States)

    Calvini, Rosalba; Foca, Giorgia; Ulrici, Alessandro

    2016-10-01

    Hyperspectral sensors represent a powerful tool for chemical mapping of solid-state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real-time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three-dimensional data array corresponding to each hyperspectral image into a one-dimensional signal (1D-DR), which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D-DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D-DR methods: average spectrum (AS), single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH). In particular, we have considered 370 NIR-hyperspectral images of a set of green coffee samples, and the three 1D-DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares-discriminant analysis were used to compare the three separate DR methods. Furthermore, low-level and mid-level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical Abstract Key steps in hyperspectral data dimenionality reduction.

  16. Block Fusion on Dynamically Adaptive Spacetree Grids for Shallow Water Waves

    KAUST Repository

    Weinzierl, Tobias

    2014-09-01

    © 2014 World Scientific Publishing Company. Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Even though they directly yield a mesh, it is often computationally reasonable to embed regular Cartesian blocks into their leaves. This promotes stencils working on homogeneous data chunks. The choice of a proper block size is sensitive. While large block sizes foster loop parallelism and vectorisation, they restrict the adaptivity\\'s granularity and hence increase the memory footprint and lower the numerical accuracy per byte. In the present paper, we therefore use a multiscale spacetree-block coupling admitting blocks on all spacetree nodes. We propose to find sets of blocks on the finest scale throughout the simulation and to replace them by fused big blocks. Such a replacement strategy can pick up hardware characteristics, i.e. which block size yields the highest throughput, while the dynamic adaptivity of the fine grid mesh is not constrained - applications can work with fine granular blocks. We study the fusion with a state-of-the-art shallow water solver at hands of an Intel Sandy Bridge and a Xeon Phi processor where we anticipate their reaction to selected block optimisation and vectorisation.

  17. Embry-Riddle Aeronautical University multispectral sensor and data fusion laboratory: a model for distributed research and education

    Science.gov (United States)

    McMullen, Sonya A. H.; Henderson, Troy; Ison, David

    2017-05-01

    The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for "virtual" modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi

  18. Stochastic approximation methods for fusion-rule estimation in multiple sensor systems

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.

    1994-06-01

    A system of N sensors S{sub 1}, S{sub 2},{hor_ellipsis},S{sub N} is considered; corresponding to an object with parameter x {element_of} {Re}{sup d}, sensor S{sub i} yields output y{sup (i)}{element_of}{Re}{sup d} according to an unknown probability distribution p{sub i}(y{sup (i)}{vert_bar}x). A training l-sample (x{sub 1}, y{sub 1}), (x{sub 2}, y{sub 2}),{hor_ellipsis},(x{sub l}, y{sub l}) is given where y{sub i} = (y{sub i}({sup 1}), y{sub i}({sup 2}),{hor_ellipsis},y{sub i}({sup N}) and y{sub i}({sup j}) is the output of S{sub j} in response to input X{sub i}. The problem is to estimate a fusion rule f : {Re}{sup Nd} {yields} {Re}{sup d}, based on the sample, such that the expected square error I(f) = {integral}[x {minus} f(y{sup 1}, y{sup 2},{hor_ellipsis},y{sup N})]{sup 2} p(y{sup 1}, y{sup 2},{hor_ellipsis},y{sup N}){vert_bar}x)p(x)dy{sup 1}dy{sup 2} {hor_ellipsis} dy{sup N}dx is to be minimized over a family of fusion rules {lambda} based on the given l-sample. Let f{sub *} {element_of} {lambda} minimize I(f); f{sub *} cannot be computed since the underlying probability distributions are unknown. Three stochastic approximation methods are presented to compute {cflx f}, such that under suitable conditions, for sufficiently large sample, P[I{cflx f} {minus} I(f{sub *}) > {epsilon}] < {delta} for arbitrarily specified {epsilon} > 0 and {delta}, 0 < {delta} < 1. The three methods are based on Robbins-Monro style algorithms, empirical risk minimization, and regression estimation algorithms.

  19. Fusion rule estimation in multiple sensor systems with unknown noise distributions

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.

    1993-12-31

    A system of N sensors S{sub 1},S{sub 2},{hor_ellipsis},S{sub N} is considered; corresponding to an object with parameter x {epsilon} R{sup d}, sensor S{sub i} yields output y{sup (i)} {epsilon} R{sup d} according to an unknown probability distribution p{sub i}(y{sup (i)}{vert_bar}x). A training l-sample (x{sub 1},y{sub 1}),(x{sub 2},y{sub 2}),{hor_ellipsis},(x{sub l},y{sub l}) is given where y{sub i} = (y{sub i}{sup (1)}, y{sub i}{sup (2)},{hor_ellipsis},y{sub i}{sup (N)}) and y{sub i}{sup (j)} is the output of S{sub j} in response to input x{sub i}. The problem is to estimate a fusion rule f:R{sup Nd} {yields} R{sup d}, based on the sample, such that the expected square error I(f) = {integral}[x {minus} f(y{sup (1)},y{sup (2)}, {hor_ellipsis},y{sup (N)})]{sup 2}p(y{sup (1)},y{sup (2)}, {hor_ellipsis},y{sup (N)}{vert_bar}x)p(x)dy{sup (1)}dy{sup (2)}{hor_ellipsis}dy{sup (N)}dx is to be minimized over a family of fusion rules {Lambda} based on the given l-sample. Let f{sub *} {epsilon} {Lambda} minimize I(f); f{sub *} cannot be computed since the underlying probability distributions are unknown. Using Vapnik`s empirical risk minimization method, we show that if {Lambda} has finite capacity, then under bounded error, for sufficiently large sample, f{sub emp} can be obtained such that P[I(f{sub emp}) {minus} I(f{sub *}) > {epsilon}] < {delta} for arbitrarily specified {epsilon} > 0 and {delta}, 0 < {delta} < 1. We identify several computational methods to obtain f{sub emp} or its approximations based on neural networks, radial basis functions, wavelets, non-polynomial networks, and polynomials and splines. We then discuss linearly separable systems to identify objects from a finite class where f{sub emp} can be computed in polynomial time using quadratic programming methods.

  20. Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter.

    Science.gov (United States)

    Atrsaei, Arash; Salarieh, Hassan; Alasty, Aria

    2016-09-01

    Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

  1. Experimental measurement of oil-water two-phase flow by data fusion of electrical tomography sensors and venturi tube

    Science.gov (United States)

    Liu, Yinyan; Deng, Yuchi; Zhang, Maomao; Yu, Peining; Li, Yi

    2017-09-01

    Oil-water two-phase flows are commonly found in the production processes of the petroleum industry. Accurate online measurement of flow rates is crucial to ensure the safety and efficiency of oil exploration and production. A research team from Tsinghua University has developed an experimental apparatus for multiphase flow measurement based on an electrical capacitance tomography (ECT) sensor, an electrical resistance tomography (ERT) sensor, and a venturi tube. This work presents the phase fraction and flow rate measurements of oil-water two-phase flows based on the developed apparatus. Full-range phase fraction can be obtained by the combination of the ECT sensor and the ERT sensor. By data fusion of differential pressures measured by venturi tube and the phase fraction, the total flow rate and single-phase flow rate can be calculated. Dynamic experiments were conducted on the multiphase flow loop in horizontal and vertical pipelines and at various flow rates.

  2. Adaptive Information Access on Multiple Applications Support Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2014-01-01

    information is challenged by dynamic nature of information elements. These challenges are more prominent in case of wireless sensor network (WSN) applications, as the information that the sensor node collects are mostly dynamic in nature (say, temperature). Therefore, it is likely that there can be a mismatch...

  3. Adaptive Information Access in Multiple Applications Support Wireless Sensor Network

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2012-01-01

    Nowadays, due to wide applicability of Wireless Sensor Network (WSN) added by the low cost sensor devices, its popularity among the researchers and industrialists are very much visible. A substantial amount of works can be seen in the literature on WSN which are mainly focused on application...

  4. Statistical Sensor Fusion of a 9-DOF Mems Imu for Indoor Navigation

    Science.gov (United States)

    Chow, J. C. K.

    2017-09-01

    Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8-89.5 % and the dead-reckoned positioning accuracy by 72.9-92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.

  5. Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

    Science.gov (United States)

    Mokhlespour Esfahani, Mohammad Iman; Zobeiri, Omid; Moshiri, Behzad; Narimani, Roya; Mehravar, Mohammad; Rashedi, Ehsan; Parnianpour, Mohamad

    2017-01-01

    Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions. PMID:28075342

  6. STATISTICAL SENSOR FUSION OF A 9-DOF MEMS IMU FOR INDOOR NAVIGATION

    Directory of Open Access Journals (Sweden)

    J. C. K. Chow

    2017-09-01

    Full Text Available Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.. This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8–89.5 % and the dead-reckoned positioning accuracy by 72.9–92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.

  7. Sensors Fusion based Online Mapping and Features Extraction of Mobile Robot in the Road Following and Roundabout

    Science.gov (United States)

    Ali, Mohammed A. H.; Mailah, Musa; Yussof, Wan Azhar B.; Hamedon, Zamzuri B.; Yussof, Zulkifli B.; Majeed, Anwar P. P.

    2016-02-01

    A road feature extraction based mapping system using a sensor fusion technique for mobile robot navigation in road environments is presented in this paper. The online mapping of mobile robot is performed continuously in the road environments to find the road properties that enable the robot to move from a certain start position to pre-determined goal while discovering and detecting the roundabout. The sensors fusion involving laser range finder, camera and odometry which are installed in a new platform, are used to find the path of the robot and localize it within its environments. The local maps are developed using camera and laser range finder to recognize the roads borders parameters such as road width, curbs and roundabout. Results show the capability of the robot with the proposed algorithms to effectively identify the road environments and build a local mapping for road following and roundabout.

  8. Multi-sensor fusion system using wavelet-based detection algorithm applied to physiological monitoring under high-G environment

    Science.gov (United States)

    Ryoo, Han Chool

    2000-06-01

    A significant problem in physiological state monitoring systems with single data channels is high rates of false alarm. In order to reduce false alarm probability, several data channels can be integrated to enhance system performance. In this work, we have investigated a sensor fusion methodology applicable to physiological state monitoring, which combines local decisions made from dispersed detectors. Difficulties in biophysical signal processing are associated with nonstationary signal patterns and individual characteristics of human physiology resulting in nonidentical observation statistics. Thus a two compartment design, a modified version of well established fusion theory in communication systems, is presented and applied to biological signal processing where we combine discrete wavelet transforms (DWT) with sensor fusion theory. The signals were decomposed in time-frequency domain by discrete wavelet transform (DWT) to capture localized transient features. Local decisions by wavelet power analysis are followed by global decisions at the data fusion center operating under an optimization criterion, i.e., minimum error criterion (MEC). We used three signals acquired from human volunteers exposed to high-G forces at the human centrifuge/dynamic flight simulator facility in Warminster, PA. The subjects performed anti-G straining maneuvers to protect them from the adverse effects of high-G forces. These maneuvers require muscular tensing and altered breathing patterns. We attempted to determine the subject's state by detecting the presence or absence of the voluntary anti-G straining maneuvers (AGSM). During the exposure to high G force the respiratory patterns, blood pressure and electroencephalogram (EEG) were measured to determine changes in the subject's state. Experimental results show that the probability of false alarm under MEC can be significantly reduced by applying the same rule found at local thresholds to all subjects, and MEC can be employed as a

  9. Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis

    Science.gov (United States)

    Markovic, Marko; Dosen, Strahinja; Popovic, Dejan; Graimann, Bernhard; Farina, Dario

    2015-12-01

    Objective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

  10. Multi-sensors data and information fusion algorithm for indoor localization

    Directory of Open Access Journals (Sweden)

    XIA Jun

    2015-02-01

    Full Text Available The localization algorithm of based on IMU is one of autonomous localization methods while it possesses the disadvantage of drift error and accumulated error,so this paper proposes a multi-sensors including wearable multi-IMUs and IWSN data and information fusion algorithm for indoor localization.On the one hand,almost all indoor localization algorithms based on IMU use only one IMU while this single-IMU-based algorithm can′t judge the posture of person precisely,one appropriate solution is that we can utilize multi-IMUs to cooperate in localization process,besides,we can fuse position information of multi-IMUs by fuzzy voting scheme.On the other hand,in order to overcome the disadvantage of drift error and accumulate error,combining with IWSN in indoor and fusing the position information calculated by IWSN and multi-IMUs via Kalman Filter algorithm.Experiment results show that the proposed indoor localization algorithm possesses good property in judging posture of person and decreasing drift error and accumulate error comparing with traditional IMU-based indoor localization algorithm.

  11. A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

    Directory of Open Access Journals (Sweden)

    Baojun Chen

    2014-07-01

    Full Text Available Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.

  12. An Adaptive Fuzzy Control Approach for the Robust Tracking of a MEMS Gyroscope Sensor

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2011-11-01

    Full Text Available In this paper, a direct adaptive fuzzy control using a supervisory compensator is designed for the robust tracking of a MEMS gyroscope sensor. The parameters of the membership functions are adjusted according to the designed adaptive law for the purpose of tracking a reference trajectory. A fuzzy controller that can approximate the unknown nonlinear function and compensate the system

  13. Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2015-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be

  14. Real-time EO/IR sensor fusion on a portable computer and head-mounted display

    Science.gov (United States)

    Yue, Zhanfeng; Topiwala, Pankaj

    2007-04-01

    Multi-sensor platforms are widely used in surveillance video systems for both military and civilian applications. The complimentary nature of different types of sensors (e.g. EO and IR sensors) makes it possible to observe the scene under almost any condition (day/night/fog/smoke). In this paper, we propose an innovative EO/IR sensor registration and fusion algorithm which runs real-time on a portable computing unit with head-mounted display. The EO/IR sensor suite is mounted on a helmet for a dismounted soldier and the fused scene is shown in the goggle display upon the processing on a portable computing unit. The linear homography transformation between images from the two sensors is precomputed for the mid-to-far scene, which reduces the computational cost for the online calibration of the sensors. The system is implemented in a highly optimized C++ code, with MMX/SSE, and performing a real-time registration. The experimental results on real captured video show the system works very well both in speed and in performance.

  15. Adaptive Piezoelectric Circuitry Sensor Network with High-Frequency Harmonics Interrogation for Structural Damage Detection

    Science.gov (United States)

    2014-09-17

    AFRL-OSR-VA-TR-2014-0255 ADAPTIVE PIEZOELECTRIC CIRCUITRY SENSOR NETWORK KON-WELL WANG MICHIGAN UNIV ANN ARBOR Final Report 09/17/2014 DISTRIBUTION A...by ANSI Std. Z39.18 09-09-2014 Final Performance Report 06-01-2011 - 05-31-2014 Adaptive Piezoelectric Circuitry Sensor Network with High-Frequency...approach. Specifically, we propose to create a new concept of adaptive high-frequency piezoelectric self-sensing interrogation by means of tunable

  16. The research of auto-focusing method for the image mosaic and fusion system with multi-sensor

    Science.gov (United States)

    Pang, Ke; Yao, Suying; Shi, Zaifeng; Xu, Jiangtao; Liu, Jiangming

    2013-09-01

    In modern image processing, due to the development of digital image processing, the focus of the sensor can be automatically set by the digital processing system through computation. In the other hand, the auto-focusing synchronously and consistently is one of the most important factors for image mosaic and fusion processing, especially for the system with multi-sensor which are put on one line in order to gain the wide angle video information. Different images sampled by the sensors with different focal length values will always increase the complexity of the affine matrix of the image mosaic and fusion in next, which potentially reducing the efficiency of the system and consuming more power. Here, a new fast evaluation method based on the gray value variance of the image pixel is proposed to find the common focal length value for all sensors to achieve the better image sharpness. For the multi-frame pictures that are sampled from different sensors that have been adjusted and been regarded as time synchronization, the gray value variances of the adjacent pixels are determined to generate one curve. This curve is the focus measure function which describes the relationship between the image sharpness and the focal length value of the sensor. On the basis of all focus measure functions of all sensors in the image processing system, this paper uses least square method to carry out the data fitting to imitate the disperse curves and give one objective function for the multi-sensor system, and then find the optimal solution corresponding to the extreme value of the image sharpness according to the evaluation of the objective function. This optimal focal length value is the common parameter for all sensors in this system. By setting the common focal length value, in the premise of ensuring the image sharpness, the computing of the affine matrix which is the core processing of the image mosaic and fusion which stitching all those pictures into one wide angle image will be

  17. Development of a Pedestrian Indoor Navigation System Based on Multi-Sensor Fusion and Fuzzy Logic Estimation Algorithms

    Science.gov (United States)

    Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.

    2015-05-01

    This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its

  18. DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Y. C. Lai

    2015-05-01

    Full Text Available This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS. There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system

  19. Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

    Directory of Open Access Journals (Sweden)

    Latifah Munirah Kamarudin

    2012-05-01

    Full Text Available In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8 were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

  20. An on-line adaptive estimation method for water holdup measurement in oil-water two-phase flow with a conductance/capacitance sensor

    Science.gov (United States)

    Wu, Hao; Tan, Chao; Dong, Feng

    2016-07-01

    Oil-water two-phase flow widely exists in industrial processes such as petroleum engineering and chemical engineering. Accurate and real-time measurement of water holdup is an important problem requiring urgent solutions. In this work, a conductance and capacitance combination sensor (CCCS) system with four conductance rings and two concave capacitance plates was validated for its measurement performance of in situ water holdup through dynamic experiments. An online adaptive weight Kalman estimation (OAWKE) fusion algorithm for the CCCS system is proposed to fuse the conductance measurement and capacitance measurement. The algorithm has fast and dynamic response for the water holdup measurement of oil-water two-phase flow and has improved measurement precision by the adaptive data fusion method. The OAWKE fusion algorithm also has the ability to deal with the abrupt change of water holdup during the measurement process. Therefore, in the low water conductivity condition (tap water), the CCCS system with the OAWKE fusion algorithm can dynamically estimate the real-time full range water holdup of oil-water two-phase flow, which has prospects in the petroleum industry.

  1. Adaptive Information Access on Multiple Applications Support Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2014-01-01

    information is challenged by dynamic nature of information elements. These challenges are more prominent in case of wireless sensor network (WSN) applications, as the information that the sensor node collects are mostly dynamic in nature (say, temperature). Therefore, it is likely that there can be a mismatch...... information access mechanism and show how trade-off between energy consumption and information reliability can be achieved....

  2. Sensors in Unmanned Robotic Vehicle

    Directory of Open Access Journals (Sweden)

    B. Rohini

    2008-05-01

    Full Text Available Unmanned tracked vehicles are developed for deployment in dangerous zones that are notsafe for human existence. These vehicles are to be fitted with various sensors for safe manoeuvre.Wide range of sensors for vehicle control, vision, and navigation are employed. The main purposeof the sensors is to infer the intended parameter precisely for further utilisation. Software isinseparable part of the sensors and plays major role in scaling, noise reduction, and fusion.Sensor fusion is normally adapted to enhance the decision-making. Vehicle location  andorientation can be sensed through global positioning system, accelerometer, gyroscope, andcompass. The unmanned vehicle can be navigated with the help of CCD camera, radar, lidar,ultrasonic sensor, piezoelectric sensor, microphone, etc.  Proximity sensors like capacitive andRF proximity detectors can detect obstacles in close vicinity.  This paper presents an overviewof sensors normally deployed in unmanned tracked vehicles.

  3. Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Doopalam Tuvshinjargal

    2015-01-01

    Full Text Available A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

  4. 国外手爪中多传感器数据融合技术的研究概况%An overseas survey of multi-sensor integration and data fusion techniques of robotics gripper

    Institute of Scientific and Technical Information of China (English)

    童利标; 徐科军; 梅涛

    2001-01-01

    近年来,多传感器数据融合技术已引起世界范围 内的普遍关注,成为一个新兴的技术方向,并已成功地应用在军事系统、交通系统和智能机 器人等研究领域。手爪是空间智能机器人的关键部件之一,它集成了视觉、触觉和力觉等多 种传感器。为了能进行灵活和自适应的操作,机器人手爪必须要采用多传感器数据融合技术 。该文介绍了机器人手爪中多传感器集成和数据融合技术的研究概况,对我国机器人的研究 发展具有一定的借鉴意义。%The multi-sensor data fusion technique, which has been applied to the research fields sucessfully such as military systems, traffic systems, intelligent robots, etc, has become a great attention all over the world and has been a new research direction in rec ent years. The gripper is one of the key components of space intelligent robot w hich integrates many sensors such as vision sensors, tactile sensors,force senso rs, etc. In order to complete the flexible and adaptive operations, the grip per must adopt sensor data fusion techniques. In this paper, a survey of multi- sensor integration and data fusion techniques is made,which may be a reference t o the development of robot in China.

  5. Sensor data fusion and image processing for object and hazard detection; Sensordatenfusion und Bildverarbeitung zur Objekt- und Gefahrenerkennung

    Energy Technology Data Exchange (ETDEWEB)

    Catala Prat, Alvaro

    2011-03-15

    The present work deals with automatic detection and tracking of objects in driving situations as well as derivation of potential hazards. To do this, the data of a laser scanner and a camera is processed and fused. The work provides new methods in the area of immediate environment detection and modeling. Thus, it creates a basis for innovative driver assistance and automation systems. The aim of such systems is to improve driving safety, traffic efficiency and driving comfort. The methods introduced in this work can be classified into different abstraction levels: At sensor data level, the data is prepared and reduced. In this work, the focus is especially set on the detection of driving oscillations from camera images and on the detection of the driving corridor from the data of different sensors, used later as the primary area of interest. At object level the central data fusion is done. High reliability, availability and sensor independency are achieved by choosing a competitive object fusion approach. As an input of the data fusion, object observations from camera and laser scanner data are extracted. These are then fused at the aim of object detection and tracking, where aspects such as robustness against manoeuvring objects, measurement outliers, split and merge effects, as well as partial object observability are addressed. At application level, early detection of potential hazards is addressed. A statistical approach has been chosen and developed, in which hazards are handled as atypical situations. This general and expandable approach is exemplarily shown based on the detected object data. The presented strategies and methods have been developed systematically, implemented in a modular prototype and tested with simulated and real data. The test results of the data fusion system show a win in data quality and robustness, with which an improvement of driver assistance and automation systems can be reached. (orig.)

  6. Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM Tasks in Autonomous Mobile Robots

    Directory of Open Access Journals (Sweden)

    Xinzheng Zhang

    2012-01-01

    Full Text Available This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM in dynamic environments. The designed approach consists of two features: (i the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM that incorporates two individual Extended Kalman Filter (EKF based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

  7. Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

    Science.gov (United States)

    Zhang, Xinzheng; Rad, Ahmad B; Wong, Yiu-Kwong

    2012-01-01

    This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

  8. New results and implications for lunar crustal iron distribution using sensor data fusion techniques

    Science.gov (United States)

    Clark, P. E.; McFadden, L. A.

    2000-02-01

    Remote measurements of the Moon have provided iron maps, and thus essential constraints for models of lunar crustal formation and mare basalt petrogenesis. A bulk crustal iron map was produced for the equatorial region from Apollo gamma-ray (AGR) spectrometer measurements, and a global iron variation map from recent Clementine spectral reflectance (CSR) measurements. Both iron maps show bimodal distribution, but have significantly different peak values and variations. In this paper, CSR data have been recalibrated to pyroxene in lunar landing site soils. A residual iron map is derived from the difference between AGR (bulk) and recalibrated CSR (pyroxene) iron abundances. The most likely interpretation is that the residual represents ferrous iron in olivine. This residual iron is anticorrelated to basin age, with older basins containing less olivine, suggesting segregation of basin basalt sources from a progressively fractionating underlying source region at the time of basin formation. Results presented here provide a quantitative basis for (1) establishing the relationship between direct geochemical (gamma-ray, X-ray) and mineralogical (near-IR) remote sensing data sets using sensor data fusion techniques to allow (2) simultaneous determination of elemental and mineralogical component distribution on remote targets and (3) meaningful interpretation of orbital and ground-based spectral reflectance measurements. When calibrated data from the Lunar Prospector mission are available, mapping of bulk crustal iron and iron-bearing soil components will be possible for the entire Moon. Similar analyses for data from the Near Earth Asteroid Rendezvous (NEAR) mission to asteroid 433 Eros will constrain models of asteroid formation.

  9. A Sensor Fusion Algorithm for Filtering Pyrometer Measurement Noise in the Czochralski Crystallization Process

    Directory of Open Access Journals (Sweden)

    M. Komperød

    2011-01-01

    Full Text Available The Czochralski (CZ crystallization process is used to produce monocrystalline silicon for solar cell wafers and electronics. Tight temperature control of the molten silicon is most important for achieving high crystal quality. SINTEF Materials and Chemistry operates a CZ process. During one CZ batch, two pyrometers were used for temperature measurement. The silicon pyrometer measures the temperature of the molten silicon. This pyrometer is assumed to be accurate, but has much high-frequency measurement noise. The graphite pyrometer measures the temperature of a graphite material. This pyrometer has little measurement noise. There is quite a good correlation between the two pyrometer measurements. This paper presents a sensor fusion algorithm that merges the two pyrometer signals for producing a temperature estimate with little measurement noise, while having significantly less phase lag than traditional lowpass- filtering of the silicon pyrometer. The algorithm consists of two sub-algorithms: (i A dynamic model is used to estimate the silicon temperature based on the graphite pyrometer, and (ii a lowpass filter and a highpass filter designed as complementary filters. The complementary filters are used to lowpass-filter the silicon pyrometer, highpass-filter the dynamic model output, and merge these filtered signals. Hence, the lowpass filter attenuates noise from the silicon pyrometer, while the graphite pyrometer and the dynamic model estimate those frequency components of the silicon temperature that are lost when lowpass-filtering the silicon pyrometer. The algorithm works well within a limited temperature range. To handle a larger temperature range, more research must be done to understand the process' nonlinear dynamics, and build this into the dynamic model.

  10. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors.

    Science.gov (United States)

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-03-28

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  11. Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.

    Science.gov (United States)

    Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem

    2015-03-01

    This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples.

  12. Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective.

    Science.gov (United States)

    Iida, Fumiya; Nurzaman, Surya G

    2016-08-01

    Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception.

  13. Adaptive AOA-aided TOA self-positioning for mobile wireless sensor networks.

    Science.gov (United States)

    Wen, Chih-Yu; Chan, Fu-Kai

    2010-01-01

    Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.

  14. Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2013-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

  15. Online Adaptive Decision Fusion Framework Based on Entropic Projections onto Convex Sets with Application to Wildfire Detection in Video

    CERN Document Server

    Gunay, Osman; Kose, Kivanc; Cetin, A Enis

    2011-01-01

    In this paper, an Entropy functional based online Adaptive Decision Fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms each of which yielding its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are...

  16. Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter

    CERN Document Server

    Fard, Ali P

    2012-01-01

    Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...

  17. Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

    Institute of Scientific and Technical Information of China (English)

    QI Wen-Juan; ZHANG Peng; DENG Zi-Li

    2014-01-01

    This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

  18. Delineation of Management Zones in Precision Agriculture by Integration of Proximal Sensing with Multivariate Geostatistics. Examples of Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Annamaria Castrignanò

    2015-07-01

    Full Text Available Fundamental to the philosophy of Precision Agriculture (PA is the concept of matching inputs to needs. Recent research in PA has focused on use of Management Zones (MZ that are field areas characterised by homogeneous attributes in landscape and soil conditions. Proximal sensing (such as Electromagnetic Induction (EMI, Ground Penetrating Radar (GPR and X-ray fluorescence can complement direct sampling and a multisensory platform can enable us to map soil features unambiguously. Several methods of multi-sensor data analysis have been developed to determine the location of subfield areas. Modern geostatistical techniques, treating variables as continua in a joint attribute and geographic space, offer the potential to analyse such data effectively. The objective of the paper is to show the potential of multivariate geostatistics to create MZ in the perspective of PA by integrating field data from different types of sensors, describing two study cases. In particular, in the first case study, cokriging and factorial cokriging were employed to produce thematic maps of soil trace elements and to delineate homogenous zones, respectively. In the second case, a multivariate geostatistical data-fusion technique (multi collocated cokriging was applied to different geophysical sensor data (GPR and EMI, for stationary estimation of soil water content and for delineating within-field zone with different wetting degree. The results have shown that linking sensors of different type improves the overall assessment of soil and sensor data fusion could be effectively applied to delineate MZs in Precision Agriculture. However, techniques of data integration are urgently required as a result of the proliferation of data from different sources.

  19. Adaptive localization and tracking of objects in a sensor network

    OpenAIRE

    2014-01-01

    [ANGLÈS] Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions, and to pass their data through the network to a central location. These networks have applications in diverse areas including environmental, health monitoring, home automation or military. The devices that form the network have limited resources, such as power and computational capacity.\\par This thesis focus on the localization and tracking problem, presenting a method that can be used with obj...

  20. Adaptive Noise Reduction Techniques for Airborne Acoustic Sensors

    Science.gov (United States)

    2012-01-01

    8 2.3.3 Low Cost Scout UAV Acoustics System ( LOSAS ) . . . . . . . . . . . . . 8 2.3.4 Shot Spotter...detecting supersonic projectiles. 2.3.3 Low Cost Scout UAV Acoustics System ( LOSAS ) SARA, Inc. has developed an acoustic sensor package that is...www.armytimes.com/legacy/new/0-ARMYPAPER-620646.php [25] (2012) Sara, inc. [Online]. Available: http://www.sara.com/ISR/acoustic sensing/ LOSAS . html [26

  1. Sensor fault diagnosis of time-delay systems based on adaptive observer

    Institute of Scientific and Technical Information of China (English)

    YOU Fu-qiang; TIAN Zuo-hua; SHI Song-jiao

    2006-01-01

    Presents a novel approach for the sensor fault diagnosis of time-delay systems by using an adaptive observer technique. The sensor fault is modeled as an additive perturbation described by a time varying function. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robustness. The stability of fault diagnosis system is proved. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

  2. Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yubin Zhao

    2016-08-01

    Full Text Available In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér–Rao lower bound (CRLB, which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the

  3. Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks.

    Science.gov (United States)

    Zhao, Yubin; Li, Xiaofan; Zhang, Sha; Meng, Tianhui; Zhang, Yiwen

    2016-08-23

    In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér-Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for

  4. Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks

    Science.gov (United States)

    Zhao, Yubin; Li, Xiaofan; Zhang, Sha; Meng, Tianhui; Zhang, Yiwen

    2016-01-01

    In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér–Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for

  5. Applications of state estimation in multi-sensor information fusion for the monitoring of open pit mine slope deformation

    Institute of Scientific and Technical Information of China (English)

    FU Hua; LIU Yin-ping; XIAO Jian

    2008-01-01

    The traditional open pit mine slope deformation monitoring system can not use the monitoring information coming from many monitoring points at the same time,can only using the monitoring data coming from a key monitoring point, and that is to say it can only handle one-dimensional time series. Given this shortage in the monitoring,the multi-sensor information fusion in the state estimation techniques would be introduced to the slope deformation monitoring system, and by the dynamic characteristics of deformation slope, the open pit slope would be regarded as a dynamic goal, the condition monitoring of which would be regarded as a dynamic target tracking. Distributed Information fusion technology with feedback was used to process the monitoring data and on this basis Klman filtering algorithms was introduced, and the simulation examples was used to prove its effectivenes.

  6. A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

    Directory of Open Access Journals (Sweden)

    Sharly Joana Halder

    2012-01-01

    Full Text Available Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI- based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI, which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

  7. A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

    Science.gov (United States)

    Liu, Bailing; Zhang, Fumin; Qu, Xinghua

    2015-01-01

    An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

  8. Adaptive preheating duration control for low-power ambient air quality sensor networks.

    Science.gov (United States)

    Baek, Yoonchul; Atiq, Mahin K; Kim, Hyung Seok

    2014-03-20

    Ceramic gas sensors used for measuring ambient air quality have features suitable for practical applications such as healthcare and air quality management, but have a major drawback-large power consumption to preheat the sensor for accurate measurements. In this paper; the adaptive preheating duration control (APC) method is proposed to reduce the power consumption of ambient air quality sensor networks. APC reduces the duration of unnecessary preheating, thereby alleviating power consumption. Furthermore, the APC can allow systems to meet user requirements such as accuracy and periodicity factor when detecting the concentration of a target gas. A performance evaluation of the power consumption of gas sensors is conducted with various user requirements and factors that affect the preheating duration of the gas sensor. This shows that the power consumption of the APC is lower than that of continuous power supply methods and constant power supply/cutoff methods.

  9. Adaptive Preheating Duration Control for Low-Power Ambient Air Quality Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yoonchul Baek

    2014-03-01

    Full Text Available Ceramic gas sensors used for measuring ambient air quality have features suitable for practical applications such as healthcare and air quality management, but have a major drawback—large power consumption to preheat the sensor for accurate measurements. In this paper; the adaptive preheating duration control (APC method is proposed to reduce the power consumption of ambient air quality sensor networks. APC reduces the duration of unnecessary preheating, thereby alleviating power consumption. Furthermore, the APC can allow systems to meet user requirements such as accuracy and periodicity factor when detecting the concentration of a target gas. A performance evaluation of the power consumption of gas sensors is conducted with various user requirements and factors that affect the preheating duration of the gas sensor. This shows that the power consumption of the APC is lower than that of continuous power supply methods and constant power supply/cutoff methods.

  10. Secure adaptive topology control for wireless ad-hoc sensor networks.

    Science.gov (United States)

    Hsueh, Ching-Tsung; Li, Yu-Wei; Wen, Chih-Yu; Ouyang, Yen-Chieh

    2010-01-01

    This paper presents a secure decentralized clustering algorithm for wireless ad-hoc sensor networks. The algorithm operates without a centralized controller, operates asynchronously, and does not require that the location of the sensors be known a priori. Based on the cluster-based topology, secure hierarchical communication protocols and dynamic quarantine strategies are introduced to defend against spam attacks, since this type of attacks can exhaust the energy of sensor nodes and will shorten the lifetime of a sensor network drastically. By adjusting the threshold of infected percentage of the cluster coverage, our scheme can dynamically coordinate the proportion of the quarantine region and adaptively achieve the cluster control and the neighborhood control of attacks. Simulation results show that the proposed approach is feasible and cost effective for wireless sensor networks.

  11. Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

    Indian Academy of Sciences (India)

    N Shantha Kumar; T Jann

    2004-04-01

    Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kalman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5° for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field.

  12. Autonomous sensor manager agents (ASMA)

    Science.gov (United States)

    Osadciw, Lisa A.

    2004-04-01

    Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.

  13. Medical case-based retrieval: integrating query MeSH terms for query-adaptive multi-modal fusion

    Science.gov (United States)

    Seco de Herrera, Alba G.; Foncubierta-Rodríguez, Antonio; Müller, Henning

    2015-03-01

    Advances in medical knowledge give clinicians more objective information for a diagnosis. Therefore, there is an increasing need for bibliographic search engines that can provide services helping to facilitate faster information search. The ImageCLEFmed benchmark proposes a medical case-based retrieval task. This task aims at retrieving articles from the biomedical literature that are relevant for differential diagnosis of query cases including a textual description and several images. In the context of this campaign many approaches have been investigated showing that the fusion of visual and text information can improve the precision of the retrieval. However, fusion does not always lead to better results. In this paper, a new query-adaptive fusion criterion to decide when to use multi-modal (text and visual) or only text approaches is presented. The proposed method integrates text information contained in MeSH (Medical Subject Headings) terms extracted and visual features of the images to find synonym relations between them. Given a text query, the query-adaptive fusion criterion decides when it is suitable to also use visual information for the retrieval. Results show that this approach can decide if a text or multi{modal approach should be used with 77.15% of accuracy.

  14. Adaptive Home System Using Wireless Sensor Network And Multi Agent System

    Directory of Open Access Journals (Sweden)

    Jayarani Kamble

    2014-03-01

    Full Text Available Smart Home is an emerging technology growing continuously which includes number of new technologies which helps to improve human’s quality of living. This paper proposes an adaptive home system for optimum utilization of power, through Artificial Intelligence and Wireless Sensor network. Artificial Intelligence is a technology for developing adaptive system that can perceive the enviornmrnt, learn from the environment and can make decision using Rule based system.Zigbee is a wireless sensor network used to efficiently deliver solution for an energy management and efficiency for adaptive home. An algorithm used in adaptive home system is based on software agent approach that reduce the energy consumption at home by considering the user’s occupancy, temperature and user’s preferences as input to the system.

  15. Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Dragoni, Nicola

    2015-01-01

    on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...

  16. Multi-sources information fusion algorithm in airborne detection systems

    Institute of Scientific and Technical Information of China (English)

    Yang Yan; Jing Zhanrong; Gao Tian; Wang Huilong

    2007-01-01

    To aim at the multimode character of the data from the airplane detecting system, the paper combines DempsterSharer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.

  17. Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.

    Directory of Open Access Journals (Sweden)

    Mohammed Al-Medhwahi

    Full Text Available The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs. The cognitive radio sensor network (CRSN, still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications.

  18. Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.

    Science.gov (United States)

    Al-Medhwahi, Mohammed; Hashim, Fazirulhisyam; Ali, Borhanuddin Mohd; Sali, Aduwati

    2016-01-01

    The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications.

  19. Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yunfei Xu

    2011-03-01

    Full Text Available This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.

  20. Adaptive ultrasonic sensor using a fiber ring laser with tandem fiber Bragg gratings.

    Science.gov (United States)

    Liu, Tongqing; Hu, Lingling; Han, Ming

    2014-08-01

    We propose and demonstrate an intensity-demodulated fiber-optic ultrasonic sensor system that can be self-adaptive to large quasi-static background strain perturbations. The sensor system is based on a fiber ring laser (FRL) whose laser cavity includes a pair of fiber Bragg gratings (FBGs). Self-adaptive ultrasonic detection is achieved by a tandem design where the two FBGs are engineered to have differential spectral responses to ultrasonic waves and are installed side-by-side at the same location on a structure. As a result, ultrasonic waves lead to relative spectral shifts of the FBGs and modulations to the cold-cavity loss of the FRL. Ultrasonic waves can then be detected directly from the laser intensity variations in response to the cold-cavity loss modulation. The sensor system is insensitive to quasi-static background strains because they lead to identical responses of the tandem FBGs. Based on the principle, a FRL sensor system was demonstrated and tested for adaptive ultrasonic detection when large static strains as well as dynamic sinusoidal vibrations were applied to the sensor.

  1. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion.

    Science.gov (United States)

    Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

    2014-09-02

    Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers.

  2. Towards adaptive security for convergent wireless sensor networks in beyond 3G environments

    DEFF Research Database (Denmark)

    Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta

    2010-01-01

    The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context-aware adapt...

  3. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    CERN Document Server

    Wu, Guowei; Xia, Feng; Xu, Zichuan; 10.3390/s101109590

    2010-01-01

    A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  4. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zichuan Xu

    2010-10-01

    Full Text Available A high degree of reliability for critical data transmission is required in body sensor networks (BSNs. However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  5. Developing paradigms of data fusion for sensor-actuator networks that perform engineering tasks.

    OpenAIRE

    Iyengar, SS; Sastry, S.; Balakrishnan, N.

    2003-01-01

    In this article we provided a new foundation for data fusion based on two concepts: a conceptual framework and the goal-seeking paradigm. The conceptual framework emphasizes the dominant structures in the system. The goal-seeking paradigm is a mechanism for representing system evolution that explicitly manages uncertainty. The goal-seeking formulation for data fusion helps to distinguish between subjective decisions that resolve uncertainty by involving humans and objective decisions that can...

  6. Smart image sensor with adaptive correction of brightness

    Science.gov (United States)

    Paindavoine, Michel; Ngoua, Auguste; Brousse, Olivier; Clerc, Cédric

    2012-03-01

    Today, intelligent image sensors require the integration in the focal plane (or near the focal plane) of complex algorithms for image processing. Such devices must meet the constraints related to the quality of acquired images, speed and performance of embedded processing, as well as low power consumption. To achieve these objectives, analog pre-processing are essential, on the one hand, to improve the quality of the images making them usable whatever the light conditions, and secondly, to detect regions of interest (ROIs) to limit the amount of pixels to be transmitted to a digital processor performing the high-level processing such as feature extraction for pattern recognition. To show that it is possible to implement analog pre-processing in the focal plane, we have designed and implemented in 130nm CMOS technology, a test circuit with groups of 4, 16 and 144 pixels, each incorporating analog average calculations.

  7. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    Directory of Open Access Journals (Sweden)

    Zhiwen Liu

    2015-08-01

    Full Text Available Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM based on an ant colony optimization algorithm (ACO-RVM for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM. RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV.

  8. Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

    Science.gov (United States)

    Deng, Xinyang; Jiang, Wen

    2017-09-12

    Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

  9. Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

    Directory of Open Access Journals (Sweden)

    Matthew Rhudy

    2011-01-01

    Full Text Available Using an Unscented Kalman Filter (UKF as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF, was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV. The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

  10. Fiber Bragg grating dynamic strain sensor using an adaptive reflective semiconductor optical amplifier source.

    Science.gov (United States)

    Wei, Heming; Tao, Chuanyi; Zhu, Yinian; Krishnaswamy, Sridhar

    2016-04-01

    In this paper, a reflective semiconductor optical amplifier (RSOA) is configured to demodulate dynamic spectral shifts of a fiber Bragg grating (FBG) dynamic strain sensor. The FBG sensor and the RSOA source form an adaptive fiber cavity laser. As the reflective spectrum of the FBG sensor changes due to dynamic strains, the wavelength of the laser output shifts accordingly, which is subsequently converted into a corresponding phase shift and demodulated by an unbalanced Michelson interferometer. Due to the short transition time of the RSOA, the RSOA-FBG cavity can respond to dynamic strains at high frequencies extending to megahertz. A demodulator using a PID controller is used to compensate for low-frequency drifts induced by temperature and large quasi-static strains. As the sensitivity of the demodulator is a function of the optical path difference and the FBG spectral width, optimal parameters to obtain high sensitivity are presented. Multiplexing to demodulate multiple FBG sensors is also discussed.

  11. SA-MAC:Self-Stabilizing Adaptive MAC Protocol for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    波澄; 韩君泽; 李向阳; 王昱; 肖波

    2014-01-01

    A common method of prolonging the lifetime of wireless sensor networks is to use low power duty cycling protocol. Existing protocols consist of two categories: sender-initiated and receiver-initiated. In this paper, we present SA-MAC, a self-stabilizing adaptive MAC protocol for wireless sensor networks. SA-MAC dynamically adjusts the transmission time-slot, waking up time-slot, and packet detection pattern according to current network working condition, such as packet length and wake-up patterns of neighboring nodes. In the long run, every sensor node will find its own transmission phase so that the network will enter a stable stage when the network load and qualities are static. We conduct extensive experiments to evaluate the energy consumption, packet reception rate of SA-MAC in real sensor networking systems. Our results indicate that SA-MAC outperforms other existing protocols.

  12. Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation

    Directory of Open Access Journals (Sweden)

    Hong SeungHo

    2011-01-01

    Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.

  13. 基于NSCT变换的多传感器图像融合算法%Multi-Sensor Image Fusion Algorithm Based on NSCT

    Institute of Scientific and Technical Information of China (English)

    童涛; 杨桄; 谭海峰; 任春颖

    2013-01-01

    Specific to the drawback that favoritism and average method are weak in maintaining the contrast of fusion image, a novel fusion algorithm based on Nonsubsampled Contourlet Transform(NSCT) is proposed. Firstly, the registered multi-sensor images from the same scene were transformed by Nonsubsampled Contourlet Transform. Then the high and low frequency coefficients are fused separately by using different fusion strategies: the low frequency coefficient is fused by adaptive regional energy, while the high frequency coefficient is fused by using regional energy matching with weighted mean and selection method. Finally, the target image is obtained by performing inverse Nonsubsampled Contourlet Transform. The algorithm has been used to merge infrared and visible images and multi-focus images. The experimental results indicate that the fused image obtained by the proposed method has a better subjective visual effect and objective evaluation criteria and performs better than traditional fusion methods.%针对多传感器图像融合中偏袒法和平均法易削减图像对比度的缺点,提出一种基于NSCT变换的图像融合算法.首先对来自同一场景配准后的多传感器图像进行NSCT变换;然后采取不同的融合策略分别对低频和高频方向子带系数进行融合:低频子带系数采用区域能量自适应加权的方法,高频方向子带系数采用局部区域能量匹配的加权平均法与选择法相结合的方案;最后通过NSCT逆变换得到融合图像.分别对红外与可见光图像和多聚焦图像融合进行实验,并对融合图像进行主客观评价,实验结果表明:该算法得到的融合图像具有较好的主观视觉效果和客观量化指标,并优于传统的融合方法.

  14. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    Directory of Open Access Journals (Sweden)

    Bruno Srbinovski

    2016-03-01

    Full Text Available Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind. Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources and power hungry sensors (ultrasonic wind sensor and gas sensors. The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  15. Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection

    Science.gov (United States)

    Dov, David; Talmon, Ronen; Cohen, Israel

    2016-12-01

    In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter, which, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and non-speech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.

  16. Traffic-adaptive duty cycle adaptation in TR-MAC protocol for wireless sensor networks

    NARCIS (Netherlands)

    Morshed, Sarwar; Baratchi, Mitra; Heijenk, Geert

    2016-01-01

    The Medium Access Control (MAC) layer can influence the energy consumption of a wireless sensor network (WSN) to a significant level. TR-MAC is an energy-efficient preamble sampling based MAC protocol for low power WSNs suitable for low data rate and low duty cycle scenario. However, low data rate i

  17. Advances in sensor adaptation to changes in ambient light: a bio-inspired solution - biomed 2010.

    Science.gov (United States)

    Dean, Brian; Wright, Cameron H G; Barrett, Stephen F

    2010-01-01

    Fly-inspired sensors have been shown to have many interesting qualities such as hyperacuity (or an ability to achieve movement resolution beyond the theoretical limit), extreme sensitivity to motion, and (through software simulation) image edge extraction, motion detection, and orientation and location of a line. Many of these qualities are beyond the ability of traditional computer vision sensors such as charge-coupled device (CCD) arrays. To obtain these characteristics, a prototype fly-inspired sensor has been built and tested in a laboratory environment and shows promise. Any sophisticated visual system, whether man made or natural, must adequately adapt to lighting conditions; therefore, light adaptation is a vital milestone in getting the fly eye vision sensor prototype working in real-world conditions. A design based on the common house fly, Musca domestica, was suggested in a paper presented to RMBS 2009 and showed an ability to remove 72-86% of effects due to ambient light changes. In this paper, a more advanced version of this design is discussed. This new design is able to remove 97-99% of the effects due to changes in ambient light, by more accurately approximating the light adaptation process used by the common house fly.

  18. Concepts for Sensor Data Fusion to Detect Vegetation Stress and Implications on Ecosystem Health Following Hurricane Katrina

    Science.gov (United States)

    2008-09-01

    instruments to collect data in hundreds of narrow wavelength bands and is specifically used for extracting much more detailed information than...on deriving both qualitative and quantitative land cover information from the data. ERDC TN-SWWRP-08-06 September 2008 Concepts for Sensor Data...Reflectance Index; relates to light use efficiency and photosynthetic capacity. CRI Carotenoid Reflectance Index; indicator of plant stress and adaptation

  19. Distributed service-based approach for sensor data fusion in IoT environments.

    Science.gov (United States)

    Rodríguez-Valenzuela, Sandra; Holgado-Terriza, Juan A; Gutiérrez-Guerrero, José M; Muros-Cobos, Jesús L

    2014-10-15

    The Internet of Things (IoT) enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments.

  20. Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments

    Directory of Open Access Journals (Sweden)

    Sandra Rodríguez-Valenzuela

    2014-10-01

    Full Text Available The Internet of Things (IoT enables the communication among smart objects promoting the pervasive presence around us of a variety of things or objects that are able to interact and cooperate jointly to reach common goals. IoT objects can obtain data from their context, such as the home, office, industry or body. These data can be combined to obtain new and more complex information applying data fusion processes. However, to apply data fusion algorithms in IoT environments, the full system must deal with distributed nodes, decentralized communication and support scalability and nodes dynamicity, among others restrictions. In this paper, a novel method to manage data acquisition and fusion based on a distributed service composition model is presented, improving the data treatment in IoT pervasive environments.

  1. Data fusion in multi sensor platforms for wide-area perception

    NARCIS (Netherlands)

    Polychronopoulos, A.; Floudas, N.; Amditis, A.; Bank, D.; Broek, S.P. van den

    2006-01-01

    there is a strong belief that the improvement of preventive safety applications and the extension of their operative range will be achieved by the deployment of multiple sensors with wide fields of view (FOV). The paper contributes to the solution of the problem and introduces distributed sensor dat

  2. Embedded pitch adapters: A high-yield interconnection solution for strip sensors

    Energy Technology Data Exchange (ETDEWEB)

    Ullán, M., E-mail: miguel.ullan@imb-cnm.csic.es [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Allport, P.P.; Baca, M.; Broughton, J.; Chisholm, A.; Nikolopoulos, K.; Pyatt, S.; Thomas, J.P.; Wilson, J.A. [School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom); Kierstead, J.; Kuczewski, P.; Lynn, D. [Brookhaven National Laboratory, Physics Department and Instrumentation Division, Upton, NY 11973-5000 (United States); Hommels, L.B.A. [Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Fleta, C.; Fernandez-Tejero, J.; Quirion, D. [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Bloch, I.; Díez, S.; Gregor, I.M.; Lohwasser, K. [DESY, Notkestrasse 85, 22607 Hamburg (Germany); and others

    2016-09-21

    A proposal to fabricate large area strip sensors with integrated, or embedded, pitch adapters is presented for the End-cap part of the Inner Tracker in the ATLAS experiment. To implement the embedded pitch adapters, a second metal layer is used in the sensor fabrication, for signal routing to the ASICs. Sensors with different embedded pitch adapters have been fabricated in order to optimize the design and technology. Inter-strip capacitance, noise, pick-up, cross-talk, signal efficiency, and fabrication yield have been taken into account in their design and fabrication. Inter-strip capacitance tests taking into account all channel neighbors reveal the important differences between the various designs considered. These tests have been correlated with noise figures obtained in full assembled modules, showing that the tests performed on the bare sensors are a valid tool to estimate the final noise in the full module. The full modules have been subjected to test beam experiments in order to evaluate the incidence of cross-talk, pick-up, and signal loss. The detailed analysis shows no indication of cross-talk or pick-up as no additional hits can be observed in any channel not being hit by the beam above 170 mV threshold, and the signal in those channels is always below 1% of the signal recorded in the channel being hit, above 100 mV threshold. First results on irradiated mini-sensors with embedded pitch adapters do not show any change in the interstrip capacitance measurements with only the first neighbors connected.

  3. Embedded pitch adapters: A high-yield interconnection solution for strip sensors

    Science.gov (United States)

    Ullán, M.; Allport, P. P.; Baca, M.; Broughton, J.; Chisholm, A.; Nikolopoulos, K.; Pyatt, S.; Thomas, J. P.; Wilson, J. A.; Kierstead, J.; Kuczewski, P.; Lynn, D.; Hommels, L. B. A.; Fleta, C.; Fernandez-Tejero, J.; Quirion, D.; Bloch, I.; Díez, S.; Gregor, I. M.; Lohwasser, K.; Poley, L.; Tackmann, K.; Hauser, M.; Jakobs, K.; Kuehn, S.; Mahboubi, K.; Mori, R.; Parzefall, U.; Clark, A.; Ferrere, D.; Gonzalez Sevilla, S.; Ashby, J.; Blue, A.; Bates, R.; Buttar, C.; Doherty, F.; McMullen, T.; McEwan, F.; O'Shea, V.; Kamada, S.; Yamamura, K.; Ikegami, Y.; Nakamura, K.; Takubo, Y.; Unno, Y.; Takashima, R.; Chilingarov, A.; Fox, H.; Affolder, A. A.; Casse, G.; Dervan, P.; Forshaw, D.; Greenall, A.; Wonsak, S.; Wormald, M.; Cindro, V.; Kramberger, G.; Mandić, I.; Mikuž, M.; Gorelov, I.; Hoeferkamp, M.; Palni, P.; Seidel, S.; Taylor, A.; Toms, K.; Wang, R.; Hessey, N. P.; Valencic, N.; Hanagaki, K.; Dolezal, Z.; Kodys, P.; Bohm, J.; Mikestikova, M.; Bevan, A.; Beck, G.; Milke, C.; Domingo, M.; Fadeyev, V.; Galloway, Z.; Hibbard-Lubow, D.; Liang, Z.; Sadrozinski, H. F.-W.; Seiden, A.; To, K.; French, R.; Hodgson, P.; Marin-Reyes, H.; Parker, K.; Jinnouchi, O.; Hara, K.; Bernabeu, J.; Civera, J. V.; Garcia, C.; Lacasta, C.; Marti i Garcia, S.; Rodriguez, D.; Santoyo, D.; Solaz, C.; Soldevila, U.

    2016-09-01

    A proposal to fabricate large area strip sensors with integrated, or embedded, pitch adapters is presented for the End-cap part of the Inner Tracker in the ATLAS experiment. To implement the embedded pitch adapters, a second metal layer is used in the sensor fabrication, for signal routing to the ASICs. Sensors with different embedded pitch adapters have been fabricated in order to optimize the design and technology. Inter-strip capacitance, noise, pick-up, cross-talk, signal efficiency, and fabrication yield have been taken into account in their design and fabrication. Inter-strip capacitance tests taking into account all channel neighbors reveal the important differences between the various designs considered. These tests have been correlated with noise figures obtained in full assembled modules, showing that the tests performed on the bare sensors are a valid tool to estimate the final noise in the full module. The full modules have been subjected to test beam experiments in order to evaluate the incidence of cross-talk, pick-up, and signal loss. The detailed analysis shows no indication of cross-talk or pick-up as no additional hits can be observed in any channel not being hit by the beam above 170 mV threshold, and the signal in those channels is always below 1% of the signal recorded in the channel being hit, above 100 mV threshold. First results on irradiated mini-sensors with embedded pitch adapters do not show any change in the interstrip capacitance measurements with only the first neighbors connected.

  4. Thin film metal sensors in fusion bonded glass chips for high-pressure microfluidics

    Science.gov (United States)

    Andersson, Martin; Ek, Johan; Hedman, Ludvig; Johansson, Fredrik; Sehlstedt, Viktor; Stocklassa, Jesper; Snögren, Pär; Pettersson, Victor; Larsson, Jonas; Vizuete, Olivier; Hjort, Klas; Klintberg, Lena

    2017-01-01

    High-pressure microfluidics offers fast analyses of thermodynamic parameters for compressed process solvents. However, microfluidic platforms handling highly compressible supercritical CO2 are difficult to control, and on-chip sensing would offer added control of the devices. Therefore, there is a need to integrate sensors into highly pressure tolerant glass chips. In this paper, thin film Pt sensors were embedded in shallow etched trenches in a glass wafer that was bonded with another glass wafer having microfluidic channels. The devices having sensors integrated into the flow channels sustained pressures up to 220 bar, typical for the operation of supercritical CO2. No leakage from the devices could be found. Integrated temperature sensors were capable of measuring local decompression cooling effects and integrated calorimetric sensors measured flow velocities over the range 0.5-13.8 mm s-1. By this, a better control of high-pressure microfluidic platforms has been achieved.

  5. SLEACH: Secure Low- Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-yun; YANG Li-zhen; CHEN Ke-fei

    2005-01-01

    LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol is a basic clustering-based routing protocol of sensor networks. In this paper, we present the design of SLEACH, a secure extension for the LEACH protocol. We divide SLEACH into four phases and fit inexpensive cryptographic operations to each part of the protocol functionality to create an efficient, practical protocol. Then we give security analyses of SLEACH. Our security analyses show that our scheme is robust against any external attacker or compromised nodes in the sensor network

  6. Assessment of fusion facility dose rate map using mesh adaptivity enhancements of hybrid Monte Carlo/deterministic techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ibrahim, Ahmad M., E-mail: ibrahimam@ornl.gov [Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831 (United States); Wilson, Paul P. [University of Wisconsin-Madison, 1500 Engineering Dr., Madison, WI 53706 (United States); Sawan, Mohamed E., E-mail: sawan@engr.wisc.edu [University of Wisconsin-Madison, 1500 Engineering Dr., Madison, WI 53706 (United States); Mosher, Scott W.; Peplow, Douglas E.; Grove, Robert E. [Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831 (United States)

    2014-10-15

    Highlights: •Calculate the prompt dose rate everywhere throughout the entire fusion energy facility. •Utilize FW-CADIS to accurately perform difficult neutronics calculations for fusion energy systems. •Develop three mesh adaptivity algorithms to enhance FW-CADIS efficiency in fusion-neutronics calculations. -- Abstract: Three mesh adaptivity algorithms were developed to facilitate and expedite the use of the CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques in accurate full-scale neutronics simulations of fusion energy systems with immense sizes and complicated geometries. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility and resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation. Additionally, because of the significant increase in the efficiency of FW-CADIS simulations, the three algorithms enabled this difficult calculation to be accurately solved on a regular computer cluster, eliminating the need for a world-class super computer.

  7. Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

    Directory of Open Access Journals (Sweden)

    Jie-sheng Wang

    2014-01-01

    Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, an echo state network (ESN based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO algorithm is proposed. Firstly, the color feature (saturation and brightness and texture features (angular second moment, sum entropy, inertia moment, etc. based on grey-level co-occurrence matrix (GLCM are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.

  8. Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.

    Science.gov (United States)

    Wang, Jie-sheng; Han, Shuang; Shen, Na-na

    2014-01-01

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.

  9. Optimization of Automatic Target Recognition with a Reject Option Using Fusion and Correlated Sensor Data

    Science.gov (United States)

    2005-04-25

    ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, Vol 30, No 7: 1145-1159 (1997). Brown, Gerald G. “Top Ten Secrets...Kuo C. and Karp , Sherman. “Polarimetric fusion for synthetic aperture radar target classification,” Pattern Recognition, Vol 30, No 5: 769-775

  10. Bearings Only Tracking with Fusion from Heterogenous Passive Sensors: ESM/EO and Acoustic

    Science.gov (United States)

    2017-02-01

    speed 5m/s. The simulation results were obtained from 100 Monte Carlo runs. The estimated position root mean square errors (RMSE) versus time are...ing with state-dependent propagation delay,” Proc. 17th International Conference on Information Fusion, Salamanca , Spain, Jul. 2014. [22] Yang, R

  11. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Amor Chowdhury

    2016-09-01

    Full Text Available The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  12. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

    Science.gov (United States)

    Chowdhury, Amor; Sarjaš, Andrej

    2016-09-15

    The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  13. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion

    Energy Technology Data Exchange (ETDEWEB)

    Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng, E-mail: qschen@ujs.edu.cn

    2014-09-02

    Highlights: • To develop a novel instrumental intelligent test methodology for food sensory analysis. • A novel data fusion was used in instrumental intelligent test methodology. • Linear and nonlinear tools were comparatively used for modeling. • The instrumental test methodology can be imitative of human test behavior. - Abstract: Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers.

  14. Self-Organizing Architecture for Information Fusion in Distributed Sensor Networks

    National Research Council Canada - National Science Library

    Bajo, Javier; De Paz, Juan F; Villarrubia, Gabriel; Corchado, Juan M

    2015-01-01

    The management of heterogeneous distributed sensor networks requires new solutions that can address the problem of automatically fusing the information coming from different sources in an efficient and effective manner...

  15. Sensor Fusion Applied To Shape Sensing: Theory and Numerical Proof-of-Concept (poster)

    NARCIS (Netherlands)

    De Mooij, C.; Martinez, M.; Benedictus, R.

    2015-01-01

    Existing shape sensing methods use individual sensor types, determining either strain or displacement well, but not both. More accurate shape sensing could improve load estimation, necessary for accurate life assessment of the structure.

  16. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  17. An Adaptive Technique for a Redundant-Sensor Navigation System. Ph.D. Thesis

    Science.gov (United States)

    Chien, T. T.

    1972-01-01

    An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. The gyro navigation system is modeled as a Gauss-Markov process, with degradation modes defined as changes in characteristics specified by parameters associated with the model. The adaptive system is formulated as a multistage stochastic process: (1) a detection system, (2) an identification system and (3) a compensation system. It is shown that the sufficient statistics for the partially observable process in the detection and identification system is the posterior measure of the state of degradation, conditioned on the measurement history.

  18. QoS Adaptive Topology Configuration in Synchronous Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    杨挺; 武娇雯; 李昂; 张志东

    2010-01-01

    By using hyper-graph theory,this paper proposes a QoS adaptive topology configuration(QATC) algorithm to effectively control large-scale topology and achieve robust data transmitting in synchronous wireless sensor networks.Firstly,a concise hyper-graph model is abstracted to analyze the large-scale and high-connectivity network.Secondly,based on the control theory of biologic "Cell Mergence",a novel self-adaptive topology configuration algorithm is used to build homologous perceptive data logic sub-network ...

  19. VLA-MAC: A Variable Load Adaptive MAC Protocol for Wireless Sensor Networks

    Science.gov (United States)

    Yao, Guoliang; Liu, Hao; Chen, Hao; Shi, Longxin

    This letter presents VLA-MAC, a novel adaptive MAC protocol for wireless sensor networks that can achieve high energy efficiency and low latency in variable load conditions. In VLA-MAC, traffic load is measured online and utilized for adaptive adjustment. VLA-MAC transmits packets via a burst style to alleviate packets accumulation problem and achieve low latency in high load condition. Furthermore, it also saves obvious energy by removing unnecessary listen period in low load condition. Unlike current approach, VLA-MAC does not need to adjust duty-cycle according to load online. Simulation results based on ns-2 show the performance improvements of our protocol.

  20. Gesture-Directed Sensor-Information Fusion (GDSIF) for Protection and Communication in Hazardous Environments

    Science.gov (United States)

    2009-11-20

    G. Rogers, R. Luna, and J. Ellen, “Wireless Communication Glove Apparatus for Motion Tracking, Gesture Recognition , Data Transmission, and Reception...and easier to deploy in a variety of ways. (See, for example, [1] and [3].) The current eGloves have magnetic and motion sensors for gesture ... recognition [5], [6]. An important future step to enhance the effectiveness of the war fighter is to integrate CBRN and other sensors into the eGloves

  1. Conjugate adaptive optics in widefield microscopy with an extended-source wavefront sensor

    CERN Document Server

    Li, Jiang; Paudel, Hari; Barankov, Roman; Bifano, Thomas; Mertz, Jerome

    2015-01-01

    Adaptive optics is a strategy to compensate for sample-induced aberrations in microscopy applications. Generally, it requires the presence of "guide stars" in the sample to serve as localized reference targets. We describe an implementation of conjugate adaptive optics that is amenable to widefield (i.e. non-scanning) microscopy, and can provide aberration corrections over potentially large fields of view without the use of guide stars. A unique feature of our implementation is that it is based on wavefront sensing with a single-shot partitioned-aperture sensor that provides large dynamic range compatible with extended samples. Combined information provided by this sensor and the imaging camera enable robust image de-blurring based on a rapid estimation of sample and aberrations obtained by closed-loop feedback. We present the theoretical principle of our technique and proof of concept experimental demonstrations.

  2. Adaptive Pulsed Laser Line Extraction for Terrain Reconstruction using a Dynamic Vision Sensor

    Directory of Open Access Journals (Sweden)

    Christian eBrandli

    2014-01-01

    Full Text Available Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor’s ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500Hz were achieved using a line laser of 3mW at a distance of 45cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2mm.

  3. Forecasting routes and self-adaptation in multi-hop wireless sensor networks

    Science.gov (United States)

    Bourdenas, Themistoklis; Bergamaschi, Flavio; Wood, David; Zerfos, Petros; Sloman, Morris

    2011-06-01

    Sensor networks find application in many tactical ISR/ISTAR processes and applications. However, these processes and applications depend on reliable collection, distribution and delivery of information that, typically, travels over multiple interconnecting nodes to reach processing centres, and are susceptible to various disruptions such as the ones caused caused by message drops, packet loss and loss of connectivity due to high traffic volumes and noise on the wireless medium. In this paper, we investigate and present approaches to pro-actively adapt routing over such networks by forecasting potential faulty regions of the network based on previous trends and reorganising routing paths. We have prototyped this approach in the ITA Sensor Fabric, an evolving middleware infrastructure for sensor networks. We, further, provide some preliminary results based on simulations.

  4. Adaptive Reliable Routing Based on Cluster Hierarchy for Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chen Min

    2010-01-01

    Full Text Available As a multimedia information acquisition and processing method, wireless multimedia sensor network(WMSN has great application potential in military and civilian areas. Compared with traditional wireless sensor network, the routing design of WMSN should obtain more attention on the quality of transmission. This paper proposes an adaptive reliable routing based on clustering hierarchy named ARCH, which includes energy prediction and power allocation mechanism. To obtain a better performance, the cluster structure is formed based on cellular topology. The introduced prediction mechanism makes the sensor nodes predict the remaining energy of other nodes, which dramatically reduces the overall information needed for energy balancing. ARCH can dynamically balance the energy consumption of nodes based on the predicted results provided by power allocation. The simulation results prove the efficiency of the proposed ARCH routing.

  5. New Low Cost Structure for Dual Axis Mount Solar Tracking System Using Adaptive Solar Sensor

    DEFF Research Database (Denmark)

    Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika

    2010-01-01

    A solar tracking system is designed to optimize the operation of solar energy receivers. The objective of this paper is proposing a new tracking system structure with two axis. The success strategy of this new project focuses on the economical analysis of solar energy. Therefore it is important...... to determine the most cost effective design, to consider the costs of production and maintenance, and operating. The proposed tracking system uses a new solar sensor position with an adaptive feature....

  6. Adaptive grating interferometric sensor for NDE metrology in high energy electromagnetic environment

    Science.gov (United States)

    Dovgalenko, George; Altintepe, Kadir; Bodnar, Michael; Prokop, Joseph

    2016-08-01

    CCD cameras and CMOS devices are the major electronic components of industrial metrology, which are vulnerable to high level electromagnetic exposure. Typical sources of exposure of electronics to ionizing radiation are the Van Allen radiation belts for satellites, nuclear reactors in power plants for sensors and control circuits, particle accelerators for control electronics particularly particle detector devices, residual radiation from isotopes in chip packaging materials, cosmic radiation for spacecraft and highaltitude aircraft, and nuclear explosions for potentially all military and civilian electronics. A total dose 5 ×103 rad was delivered to silicon-based devices in seconds to minutes caused long-term degradation. We demonstrated adaptive grating, 3D image sensor for NDE metrology which is non vulnerable for high level X-Ray1 and 3 × 106 rad gamma radiation exposure. Sensor is based on adaptive holographic grating generated by 632.8 nm He-Ne laser - in doped electro optic Bismuth Titanate (BTO) monocrystal. Mathematical algorithm of bipolar model conductivity in BTO crystal has been applied for experimental analyses. Applications of proposed sensor for airspace, military, nuclear and civil engineering industries have been discussed.

  7. Accurate positioning of pedestrains in mixed indoor/outdoor settings : A particle filter approach to sensor and map fusion

    DEFF Research Database (Denmark)

    Toftkjær, Thomas

    Pedestrian positioning with full coverage in urban environments is a long sought after research goal. This thesis proposes new techniques for handling the challenging task of truly pervasive pedestrian positioning. It shows that through sensor fusion one can both improve accuracy and extend...... the coverage of pedestrian positioning, for the professional person, such as rst responders, as well as for the ordinary citizen. Since GNSS alone cannot satisfy the availability and accuracy demands for all indoor settings, this thesis pursues a better understanding of the capabilities of GNSS indoors...... and an extension to ProPosition, that advances the state-of-the-art in pedestrian positioning towards the pervasive research goals of full urban environment coverage, seamless indoor and outdoor positioning and the ability to run on ordinary user devices such as smartphones. Through ProLoc it is possible...

  8. Integration and bench testing for the GRAVITY Coudé IR adaptive optics (CIAO) wavefront sensor

    Science.gov (United States)

    Deen, C.; Yang, P.; Huber, A.; Suarez-Valles, M.; Hippler, S.; Brandner, W.; Gendron, E.; Clénet, Y.; Kendrew, S.; Glauser, A.; Klein, R.; Laun, W.; Lenzen, R.; Neumann, U.; Panduro, J.; Ramos, J.; Rohloff, R.-R.; Salzinger, A.; Zimmerman, N.; Henning, T.; Perraut, K.; Perrin, G.; Straubmeier, C.; Amorim, A.; Eisenhauer, F.

    2014-08-01

    GRAVITY, a second generation instrument for the Very Large Telescope Interferometer (VLTI), will provide an astrometric precision of order 10 micro-arcseconds, an imaging resolution of 4 milli-arcseconds, and low/medium resolution spectro-interferometry. These improvements to the VLTI represent a major upgrade to its current infrared interferometric capabilities, allowing detailed study of obscured environments (e.g. the Galactic Center, young dusty planet-forming disks, dense stellar cores, AGN, etc...). Crucial to the final performance of GRAVITY, the Coudé IR Adaptive Optics (CIAO) system will correct for the effects of the atmosphere at each of the VLT Unit Telescopes. CIAO consists of four new infrared Shack-Hartmann wavefront sensors (WFS) and associated real-time computers/software which will provide infrared wavefront sensing from 1.45-2.45 microns, allowing AO corrections even in regions where optically bright reference sources are scarce. We present here the latest progress on the GRAVITY wavefront sensors. We describe the adaptation and testing of a light-weight version of the ESO Standard Platform for Adaptive optics Real Time Applications (SPARTA-Light) software architecture to the needs of GRAVITY. We also describe the latest integration and test milestones for construction of the initial wave front sensor.

  9. Cooperative Navigation and Coverage Identification with Random Gossip and Sensor Fusion

    OpenAIRE

    2016-01-01

    This paper is concerned with cooperative Terrain Aided Navigation of a network of aircraft using fusion of Radar Altimeter and inter-node range measurements. State inference is performed using a Rao-Blackwellized Particle Filter with online measurement noise statistics estimation. For terrain coverage measurement noise parameter identification, an online Expectation Maximization algorithm is proposed, where local sufficient statistics at each node are calculated in the E-step, which are then ...

  10. A chitosan-coated humidity sensor based on Mach-Zehnder interferometer with waist-enlarged fusion bitapers

    Science.gov (United States)

    Ni, Kai; Chan, Chi Chiu; Chen, Lihan; Dong, Xinyong; Huang, Ran; Ma, Qifei

    2017-01-01

    A novel humidity sensor, which adopts a Mach-Zehnder interferometer (MZI) in normal single mode fiber (SMF) modified by the deposition of chitosan (a moisture-sensitive natural polymer) on the cladding, is proposed and experimentally demonstrated. It is fabricated by the fusion splicing of a segment between the two SMF with waist-enlarged fusion bitapers. This all-fiber MZI based on SMF incorporates intermodal interference between the core mode and the cladding mode. Due to the fact that it is sensitive to external refractive index and that the RI of the chitosan multi-layer film coat depends on the environmental humidity, the SMF-MZI with a chitosan coating layer of nanometer thickness is employed in humidity measuring. The sensitivity of ∼119.6 pm/RH (relative humidity unit) is achieved within the range from 10% to 90% on the experimental level. Moreover, the chitosan coat has good biocompatibility for in vivo biomedical applications like immunosensing and DNA hybridization detection in the near future.

  11. Non-gyroscope DR and adaptive information fusion algorithm used in GPS/DR device

    Institute of Scientific and Technical Information of China (English)

    Li Qingli; Xue Yongqi; Shang Yanlei; Shi Pengfei

    2006-01-01

    In view of the problems existing in GPS, a non-gyroscope DR is introduced. The operating principle and the algorithm of the GPS/DR device are also presented. By operating measured data synthetically, linear observation equations are obtained for the information fusion algorithm. This approach avoids model error due to linearizing nonlinear observation equations in the conventional algorithm, so that the stability of information fusion algorithm is improved and computation expenses are reduced. Field running experiments show that satisfactory accuracy can be obtained by the proposed navigation model and algorithm for the non-gyroscope GPS/DR device.

  12. A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain

    Science.gov (United States)

    Xiang, Tianzhu; Yan, Li; Gao, Rongrong

    2015-03-01

    In this paper, a novel fusion algorithm based on the adaptive dual-channel unit-linking pulse coupled neural network (PCNN) for infrared and visible images fusion in nonsubsampled contourlet transform (NSCT) domain is proposed. The flexible multi-resolution and directional expansion for images of NSCT are associated with global coupling and pulse synchronization characteristic of dual-PCNN. Compared with other dual-PCNN models, the proposed model possesses fewer parameters and is not difficult to implement adaptive, which is more suitable for image fusion. Firstly, the source images were multi-scale and multi-directional decomposed by NSCT. Then, to make dual-channel PCNN adaptive, the average gradient of each pixel was presented as the linking strength, and the time matrix was presented to determine the iteration number adaptively. In this fusion scheme, a novel sum modified-Laplacian of low-frequency subband and a modified spatial frequency of high-frequency subband were input to motivate the adaptive dual-channel unit-linking PCNN, respectively. Experimental results demonstrate that the proposed algorithm can significantly improve image fusion performance, accomplish notable target information and high contrast, simultaneously preserve rich details information, and excel other typical current methods in both objective evaluation criteria and visual effect.

  13. Particle Filter-Based Recursive Data Fusion With Sensor Indexing for Large Core Neutron Flux Estimation

    Science.gov (United States)

    Tamboli, Prakash Kumar; Duttagupta, Siddhartha P.; Roy, Kallol

    2017-06-01

    We introduce a sequential importance sampling particle filter (PF)-based multisensor multivariate nonlinear estimator for estimating the in-core neutron flux distribution for pressurized heavy water reactor core. Many critical applications such as reactor protection and control rely upon neutron flux information, and thus their reliability is of utmost importance. The point kinetic model based on neutron transport conveniently explains the dynamics of nuclear reactor. The neutron flux in the large core loosely coupled reactor is sensed by multiple sensors measuring point fluxes located at various locations inside the reactor core. The flux values are coupled to each other through diffusion equation. The coupling facilitates redundancy in the information. It is shown that multiple independent data about the localized flux can be fused together to enhance the estimation accuracy to a great extent. We also propose the sensor anomaly handling feature in multisensor PF to maintain the estimation process even when the sensor is faulty or generates data anomaly.

  14. Part task investigation of multispectral image fusion using gray scale and synthetic color night-vision sensor imagery for helicopter pilotage

    Science.gov (United States)

    Steele, Paul M.; Perconti, Philip

    1997-06-01

    Today, night vision sensor and display systems used in the pilotage or navigation of military helicopters are either long wave IR thermal sensors (8 - 12 microns) or image intensified, visible and near IR (0.6 - 0.9 microns), sensors. The sensor imagery is displayed using a monochrome phosphor on a Cathode Ray Tube or night vision goggle. Currently, there is no fielded capability to combine the best attributes of the emissive radiation sensed by the thermal sensor and the reflected radiation sensed by the image intensified sensor into a single fused image. However, recent advances in signal processing have permitted the real time image fusion and display of multispectral sensors in either monochrome or synthetic chromatic form. The merits of such signal processing is explored. A part task simulation using a desktop computer, video playback unit, and a biocular head mounted display was conducted. Response time and accuracy measures of test subject responses to visual perception tasks were taken. Subjective ratings were collected to determine levels of pilot acceptance. In general, fusion based formats resulted in better subject performance. The benefits of integrating synthetic color to fused imagery, however, is dependent on the color algorithm used, the visual task performed, and scene content.

  15. Observability considerations for multi-sensor and product fusion: Bias, information content, and validation (Invited)

    Science.gov (United States)

    Reid, J. S.; Zhang, J.; Hyer, E. J.; Campbell, J. R.; Christopher, S. A.; Ferrare, R. A.; Leptoukh, G. G.; Stackhouse, P. W.

    2009-12-01

    With the successful development of many aerosol products from the NASA A-train as well as new operational geostationary and polar orbiting sensors, the scientific community now has a host of new parameters to use in their analyses. The variety and quality of products has reached a point where the community has moved from basic observation-based science to sophisticated multi-component research that addresses the complex atmospheric environment. In order for these satellite data contribute to the science their uncertainty levels must move from semi-quantitative to quantitative. Initial attempts to quantify uncertainties have led to some recent debate in the community as to the efficacy of aerosol products from current and future NASA satellite sensors. In an effort to understand the state of satellite product fidelity, the Naval Research Laboratory and a newly reformed Global Energy and Water Cycle Experiment (GEWEX) aerosol panel have both initiated assessments of the nature of aerosol remote sensing uncertainty and bias. In this talk we go over areas of specific concern based on the authors’ experiences with the data, emphasizing the multi-sensor problem. We first enumerate potential biases, including retrieval, sampling/contextual, and cognitive bias. We show examples of how these biases can subsequently lead to the pitfalls of correlated/compensating errors, tautology, and confounding. The nature of bias is closely related to the information content of the sensor signal and its subsequent application to the derived aerosol quantity of interest (e.g., optical depth, flux, index of refraction, etc.). Consequently, purpose-specific validation methods must be employed, especially when generating multi-sensor products. Indeed, cloud and lower boundary condition biases in particular complicate the more typical methods of regressional bias elimination and histogram matching. We close with a discussion of sequestration of uncertainty in multi-sensor applications of

  16. Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter

    Science.gov (United States)

    Foxlin, Eric

    1996-01-01

    Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

  17. Sensor Integration, Management and Data Fusion Concepts in a Naval Command and Control Perspective

    Science.gov (United States)

    2016-06-07

    October I octobre 1998 Approved by I approuve par ~.rro /3.J.P..u#VU."’ & i Section Head I Chef d~ section .. le. 9 od’ohtt ~8 Date SANS...efforts to- reduce uncertainty and is invaluable in resolving ambiguities.- Complementarity results if the sensor suite is made up of sensors each of...which observes a subset of the environment state space, such that the union of these subsets makes up the whole environment state space which is of

  18. A Stable Switch Method Based on Fusion in Uncalibrated Visual Servoing

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Stable switch control between multiple cameras for uncalibrated visual servoing was studied. Switch images based on fusion were presented to get the continuous dynamic image Jacobian matrix among robots and distributed visual sensors. The designed fusion algorithm is suitable to have dynamically adjustable fusion weights,and the fusion structure was analyzed. Simulations and experiments without any knowledge of mobile robots and uncalibrated visual sensors show that the method has higher adaptability than the traditional instant switch control method. The method can enhance the system stability at the switching process.

  19. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors.

    Science.gov (United States)

    Li, Yanran; Zhang, Xu; Gong, Yanan; Cheng, Ying; Gao, Xiaoping; Chen, Xiang

    2017-03-13

    Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.

  20. A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

    Directory of Open Access Journals (Sweden)

    Leandro Vargas-Meléndez

    2016-08-01

    Full Text Available This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

  1. A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

    Science.gov (United States)

    Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

    2016-08-31

    This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

  2. A Navigation System for the Visually Impaired: A Fusion of Vision and Depth Sensor.

    Science.gov (United States)

    Kanwal, Nadia; Bostanci, Erkan; Currie, Keith; Clark, Adrian F

    2015-01-01

    For a number of years, scientists have been trying to develop aids that can make visually impaired people more independent and aware of their surroundings. Computer-based automatic navigation tools are one example of this, motivated by the increasing miniaturization of electronics and the improvement in processing power and sensing capabilities. This paper presents a complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor. The system is based around corners and depth values from Kinect's infrared sensor. Obstacles are found in images from a camera using corner detection, while input from the depth sensor provides the corresponding distance. The combination is both efficient and robust. The system not only identifies hurdles but also suggests a safe path (if available) to the left or right side and tells the user to stop, move left, or move right. The system has been tested in real time by both blindfolded and blind people at different indoor and outdoor locations, demonstrating that it operates adequately.

  3. Camera-based platform and sensor motion tracking for data fusion in a landmine detection system

    NARCIS (Netherlands)

    Mark, W. van der; Heuvel, J.C. van den; Breejen, E. den; Groen, F.C.A.

    2003-01-01

    Vehicles that serve in the role as landmine detection robots could be an important tool for demining former conflict areas. On the LOTUS platform for humanitarian demining, different sensors are used to detect a wide range of landmine types. Reliable and accurate detection depends on correctly combi

  4. PCNN-Based Image Fusion in Compressed Domain

    Directory of Open Access Journals (Sweden)

    Yang Chen

    2015-01-01

    Full Text Available This paper addresses a novel method of image fusion problem for different application scenarios, employing compressive sensing (CS as the image sparse representation method and pulse-coupled neural network (PCNN as the fusion rule. Firstly, source images are compressed through scrambled block Hadamard ensemble (SBHE for its compression capability and computational simplicity on the sensor side. Local standard variance is input to motivate PCNN and coefficients with large firing times are selected as the fusion coefficients in compressed domain. Fusion coefficients are smoothed by sliding window in order to avoid blocking effect. Experimental results demonstrate that the proposed fusion method outperforms other fusion methods in compressed domain and is effective and adaptive in different image fusion applications.

  5. Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation

    Directory of Open Access Journals (Sweden)

    Vaibhav Awale

    2015-11-01

    Full Text Available Most submarines carry more than one set of inertial navigation system (INS for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell1. The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local filters (one set of INS system integrated with external sensors, in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local filters. Hence a decentralised Kalman filter approach is considered for combining the estimates of local filters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman filter, but in that case, corruption of one INS system will lead to failure of the whole filter. Hence to ensure satisfactory performance of the filter even in the event of failure of some INS system, a decentralised Kalman filtering approach is considered.

  6. Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

    Science.gov (United States)

    K., Nirmal; A. G., Sreejith; Mathew, Joice; Sarpotdar, Mayuresh; Suresh, Ambily; Prakash, Ajin; Safonova, Margarita; Murthy, Jayant

    2016-07-01

    We describe the characterization and removal of noises present in the Inertial Measurement Unit (IMU) MPU- 6050, which was initially used in an attitude sensor, and later used in the development of a pointing system for small balloon-borne astronomical payloads. We found that the performance of the IMU degraded with time because of the accumulation of different errors. Using Allan variance analysis method, we identified the different components of noise present in the IMU, and verified the results by the power spectral density analysis (PSD). We tried to remove the high-frequency noise using smooth filters such as moving average filter and then Savitzky Golay (SG) filter. Even though we managed to filter some high-frequency noise, these filters performance wasn't satisfactory for our application. We found the distribution of the random noise present in IMU using probability density analysis and identified that the noise in our IMU was white Gaussian in nature. Hence, we used a Kalman filter to remove the noise and which gave us good performance real time.

  7. Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

    CERN Document Server

    Nirmal, K; Mathew, Joice; Sarpotdar, Mayuresh; Suresh, Ambily; Prakash, Ajin; Safonova, Margarita; Murthy, Jayant

    2016-01-01

    We describe the characterization and removal of noise present in the Inertial Measurement Unit (IMU) MPU-6050. This IMU was initially used in an attitude sensor (AS) developed in-house, and subsequently implemented in a pointing and stabilization platform developed for small balloon-borne astronomical payloads. We found that the performance of the IMU degrades with time due to the accumulation of different errors. Using the Allan variance analysis method, we identified the different components of noise present in the IMU and verified the results using a power spectral density analysis (PSD). We tried to remove the high-frequency noise using smoothing filters, such as moving average filter and Savitzky-Golay filter. Although we did manage to filter some of the high-frequency noise, the performance of these filters was not satisfactory for our application. We found the distribution of the random noise present in the IMU using a probability density analysis, and identified the noise to be white Gaussian in natur...

  8. Tomographic data fusion with CFD simulations associated with a planar sensor

    Science.gov (United States)

    Liu, J.; Liu, S.; Sun, S.; Zhou, W.; Schlaberg, I. H. I.; Wang, M.; Yan, Y.

    2017-04-01

    Tomographic techniques have great abilities to interrogate the combustion processes, especially when it is combined with the physical models of the combustion itself. In this study, a data fusion algorithm is developed to investigate the flame distribution of a swirl-induced environmental (EV) burner, a new type of burner for low NOx combustion. An electric capacitance tomography (ECT) system is used to acquire 3D flame images and computational fluid dynamics (CFD) is applied to calculate an initial distribution of the temperature profile for the EV burner. Experiments were also carried out to visualize flames at a series of locations above the burner. While the ECT images essentially agree with the CFD temperature distribution, discrepancies exist at a certain height. When data fusion is applied, the discrepancy is visibly reduced and the ECT images are improved. The methods used in this study can lead to a new route where combustion visualization can be much improved and applied to clean energy conversion and new burner development.

  9. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-09-25

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

  10. Noise in adaptive interferometric fiber sensor based on population dynamic grating in erbium-doped fiber.

    Science.gov (United States)

    Stepanov, Serguei; Sánchez, Marcos Plata; Hernández, Eliseo Hernández

    2016-09-10

    Experimental investigations of the main noise sources that limit the sensitivity of the adaptive interferometric all-fiber sensors operating in the communication wavelength region are reported. Adaptive properties (i.e., the autostabilization of an optimal operation point of the interferometer) are enabled by the dynamic population grating recorded in a segment of the erbium-doped fiber (EDF) at milliwatt-scale cw power in the 1480-1560 nm spectral range. The utilized symmetric Sagnac configuration with low light internal reflections ensures reduced sensitivity of the sensor to phase noise of the laser, while intensity noise is reduced to an insignificant level by the balanced detection scheme. It is shown that the fluorescence from the erbium ions, excited by the counterpropagating waves recording the grating, increases the noise level from the fundamental shot noise approximately by a factor of 2-3 only. It is also shown that conventional communication distributed feedback (DFB) semiconductor lasers with megahertz linewidth are not suitable for high-sensitivity applications of such sensors. Because of inevitable backreflections from the output terminal devices (photodiodes, insulators, circulator), the above-mentioned fundamental noise level is increased by 2 orders of magnitude due to high phase noise of the DFB laser.

  11. Real time implementation of adaptive sliding mode observer based speed sensor less vector control of induction motor

    Directory of Open Access Journals (Sweden)

    Negadi Karim

    2010-01-01

    Full Text Available Sensor less induction motor drives are widely used in industry for their reliability and flexibility. However, rotor flux and speed sensors are required for vector control of induction motor. These sensors are sources of trouble, mainly in hostile environments, and their application reduces the drive robustness. The cost of the sensors is not also negligible. All the reasons lead to development of different sensor less methods for rotor flux and mechanical speed estimation in electrical drives. The paper deals with the speed estimators for applications in sensor less induction motor drive with vector control, which are based on application of model adaptive, based sliding mode observer methods. This paper presents the development and DSP implementation of the speed estimators for applications in sensor less drives with induction motor.

  12. Biologically-inspired robust and adaptive multi-sensor fusion and active control

    Science.gov (United States)

    Khosla, Deepak; Dow, Paul A.; Huber, David J.

    2009-04-01

    In this paper, we describe a method and system for robust and efficient goal-oriented active control of a machine (e.g., robot) based on processing, hierarchical spatial understanding, representation and memory of multimodal sensory inputs. This work assumes that a high-level plan or goal is known a priori or is provided by an operator interface, which translates into an overall perceptual processing strategy for the machine. Its analogy to the human brain is the download of plans and decisions from the pre-frontal cortex into various perceptual working memories as a perceptual plan that then guides the sensory data collection and processing. For example, a goal might be to look for specific colored objects in a scene while also looking for specific sound sources. This paper combines three key ideas and methods into a single closed-loop active control system. (1) Use high-level plan or goal to determine and prioritize spatial locations or waypoints (targets) in multimodal sensory space; (2) collect/store information about these spatial locations at the appropriate hierarchy and representation in a spatial working memory. This includes invariant learning of these spatial representations and how to convert between them; and (3) execute actions based on ordered retrieval of these spatial locations from hierarchical spatial working memory and using the "right" level of representation that can efficiently translate into motor actions. In its most specific form, the active control is described for a vision system (such as a pantilt- zoom camera system mounted on a robotic head and neck unit) which finds and then fixates on high saliency visual objects. We also describe the approach where the goal is to turn towards and sequentially foveate on salient multimodal cues that include both visual and auditory inputs.

  13. Pose Estimation of Unmanned Aerial Vehicles Based on a Vision-Aided Multi-Sensor Fusion

    Science.gov (United States)

    Abdi, G.; Samadzadegan, F.; Kurz, F.

    2016-06-01

    GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

  14. Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

    Directory of Open Access Journals (Sweden)

    Sara Saeedi

    2015-08-01

    Full Text Available Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations.

  15. POSE ESTIMATION OF UNMANNED AERIAL VEHICLES BASED ON A VISION-AIDED MULTI-SENSOR FUSION

    Directory of Open Access Journals (Sweden)

    G. Abdi

    2016-06-01

    Full Text Available GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

  16. A Fusion Architecture for Tracking a Group of People Using a Distributed Sensor Network

    Science.gov (United States)

    2013-07-01

    assumptions in order to answer the above questions: • If somebody is riding an animal, the animal and the rider are considered as one target and...categorized as an animal, since the rider does not contribute to the seismic signals and contributes very little to PIR and ultrasonic signatures. This...these sensors brings a specific attribute to classify the targets and help in counting their number. Although we consider horses in this paper, the

  17. Multi-Sensor Image Fusion for Target Recognition in the Environment of Network Decision Support Systems

    Science.gov (United States)

    2015-12-01

    tool kit SURF Speeded-up robust features TNT Tactical network topology TP True positive TPR True positive rate UAS Unmanned airborne system UMS...can provide interconnection among such networks and existing terrestrial, satellite, or mobile Internet networks , such as 3G, 4G, or even 5G , and may...mesh sensor networks . If and when the classified COIs are positively identified, they become part of the image database, and the system has to be

  18. Low Complexity Track Initialization and Fusion for Multi-Modal Sensor Networks

    Science.gov (United States)

    2012-11-08

    that is, freely available to any visitor to IEEE Xplore . Authors who choose open access must do so by electing this option by notifying the Editor-In...Publications 1. Q. Le and L.M. Kaplan, “Target localization using proximity binary sensors,” Proceedings of IEEE Aerospace conference, Big Sky, MT... IEEE Aerospace Conference, Big Sky, MT, Mar. 2011. 5. Q. Le, and L. M. Kaplan, “Effects of Operation Parameters on Multitarget Tracking in Proximity

  19. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    Directory of Open Access Journals (Sweden)

    Mohammad Abdur Razzaque

    2014-12-01

    Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

  20. Adaptive compression of slowly varying images transmitted over Wireless Sensor Networks.

    Science.gov (United States)

    Nikolakopoulos, George; Kandris, Dionisis; Tzes, Anthony

    2010-01-01

    In this article a scheme for image transmission over Wireless Sensor Networks (WSN) with an adaptive compression factor is introduced. The proposed control architecture affects the quality of the transmitted images according to: (a) the traffic load within the network and (b) the level of details contained in an image frame. Given an approximate transmission period, the adaptive compression mechanism applies Quad Tree Decomposition (QTD) with a varying decomposition compression factor based on a gradient adaptive approach. For the initialization of the proposed control scheme, the desired a priori maximum bound for the transmission time delay is being set, while a tradeoff among the quality of the decomposed image frame and the time needed for completing the transmission of the frame should be taken under consideration. Based on the proposed control mechanism, the quality of the slowly varying transmitted image frames is adaptively deviated based on the measured time delay in the transmission. The efficacy of the adaptive compression control scheme is validated through extended experimental results.

  1. Adaptive Compression of Slowly Varying Images Transmitted over Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Anthony Tzes

    2010-07-01

    Full Text Available In this article a scheme for image transmission over Wireless Sensor Networks (WSN with an adaptive compression factor is introduced. The proposed control architecture affects the quality of the transmitted images according to: (a the traffic load within the network and (b the level of details contained in an image frame. Given an approximate transmission period, the adaptive compression mechanism applies Quad Tree Decomposition (QTD with a varying decomposition compression factor based on a gradient adaptive approach. For the initialization of the proposed control scheme, the desired a priori maximum bound for the transmission time delay is being set, while a tradeoff among the quality of the decomposed image frame and the time needed for completing the transmission of the frame should be taken under consideration. Based on the proposed control mechanism, the quality of the slowly varying transmitted image frames is adaptively deviated based on the measured time delay in the transmission. The efficacy of the adaptive compression control scheme is validated through extended experimental results.

  2. Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.

    Science.gov (United States)

    de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos

    2012-01-01

    Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

  3. Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

    Science.gov (United States)

    Gani, Abdullah; Anisi, Mohammad Hossein; Ab Hamid, Siti Hafizah; Akhunzada, Adnan; Khan, Muhammad Khurram

    2016-01-01

    A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results. PMID:27658194

  4. High-speed SPGD wavefront controller for an adaptive optics system without wavefront sensor

    Science.gov (United States)

    Wang, Caixia; Li, Xinyang; Li, Mei; Ye, Jongwei; Chen, Bo

    2010-10-01

    A non-conventional adaptive optics system based on direct system performance metric optimization is illustrated. The system does not require wave-front sensor which is difficult to work under the poor condition such as beam cleanup for the anomalous light beam. The system comprises a high speed wavefront controller based on Stochastic Parallel Gradient Descent (SPGD) Algorithm, a deformable mirror, a tip/tilt mirror and a far-field system performance metric sensor. The architecture of the wave-front controller is based on a combination of Field Programmable Gate Array (FPGA) and floating-point Digital Signal Processor (DSP). The Zernike coefficient information is applied to improve the iteration speed. The experimental results show that the beam cleanup system based on SPGD keep a high iteration speed. The controller can compensate the wavefront aberration and tilt excursion effectively.

  5. Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks.

    Science.gov (United States)

    Liaqat, Misbah; Gani, Abdullah; Anisi, Mohammad Hossein; Ab Hamid, Siti Hafizah; Akhunzada, Adnan; Khan, Muhammad Khurram; Ali, Rana Liaqat

    A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results.

  6. 3D-information fusion from very high resolution satellite sensors

    Science.gov (United States)

    Krauss, T.; d'Angelo, P.; Kuschk, G.; Tian, J.; Partovi, T.

    2015-04-01

    In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl'eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

  7. Adaptive Opportunistic Cooperative Control Mechanism Based on Combination Forecasting and Multilevel Sensing Technology of Sensors for Mobile Internet of Things

    Directory of Open Access Journals (Sweden)

    Yong Jin

    2014-01-01

    Full Text Available In mobile Internet of Things, there are many challenges, including sensing technology of sensors, how and when to join cooperative transmission, and how to select the cooperative sensors. To address these problems, we studied the combination forecasting based on the multilevel sensing technology of sensors, building upon which we proposed the adaptive opportunistic cooperative control mechanism based on the threshold values such as activity probability, distance, transmitting power, and number of relay sensors, in consideration of signal to noise ratio and outage probability. More importantly, the relay sensors would do self-test real time in order to judge whether to join the cooperative transmission, for maintaining the optimal cooperative transmission state with high performance. The mathematical analyses results show that the proposed adaptive opportunistic cooperative control approach could perform better in terms of throughput ratio, packet error rate and delay, and energy efficiency, compared with the direct transmission and opportunistic cooperative approaches.

  8. Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Shifei Liu

    2015-01-01

    Full Text Available The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS in an innovative extended Kalman filter (EKF design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy.

  9. A uniquely adaptable pore is consistent with NALCN being an ion sensor.

    Science.gov (United States)

    Senatore, Adriano; Spafford, J David

    2013-01-01

    NALCN is an intriguing, orphan ion channel among the 4x6TM family of related voltage-gated cation channels, sharing a common architecture of four homologous domains consisting of six transmembrane helices, separated by three cytoplasmic linkers and delimited by N and C-terminal ends. NALCN is one of the shortest 4x6TM family members, lacking much of the variation that provides the diverse palate of gating features, and tissue specific adaptations of sodium and calcium channels. NALCN's most distinctive feature is that that it possesses a highly adaptable pore with a calcium-like EEEE selectivity filter in radially symmetrical animals and a more sodium-like EEKE or EKEE selectivity filter in bilaterally symmetrical animals including vertebrates. Two lineages of animals evolved alternative calcium-like EEEE and sodium-like EEKE / EKEE pores, spliced to regulate NALCN functions in differing cellular environments, such as muscle (heart and skeletal) and secretory tissue (brain and glands), respectively. A highly adaptable pore in an otherwise conserved ion channel in the 4x6TM channel family is not consistent with a role for NALCN in directly gating a significant ion conductance that can be either sodium ions or calcium ions. NALCN was proposed to be an expressible Gd ( 3+) -sensitive, NMDG (+) -impermeant, non-selective and ohmic leak conductance in HEK-293T cells, but we were unable to distinguish these reported currents from leaky patch currents (ILP) in control HEK-293T cells. We suggest that NALCN functions as a sensor for the much larger UNC80/UNC79 complex, in a manner consistent with the coupling mechanism known for other weakly or non-conducting 4x6TM channel sensor proteins such as Nax or Cav 1.1. We propose that NALCN serves as a variable sensor that responds to calcium or sodium ion flux, depending on whether the total cellular current density is generated more from calcium-selective or sodium-selective channels.

  10. Biomimetic fusion that enhances sensor performance in a bimodal surveillance system

    Science.gov (United States)

    Ziph-Schatzberg, Leah; Kelsall, Sarah; Hubbard, Allyn E.

    2011-06-01

    Algorithms for synergistically fusing acoustic and optical sensory inputs, thereby mimicking biological attentional processes are described. Manual existing perimeter defense surveillance systems using more than one sensory modality combine different sensors' information to corroborate findings by other sensors and to add data from a second modality. In contrast to how conventional systems work, animals use information from multiple sensory inputs in a way that improves each sensory system's performance. We demonstrated that performance is enhanced when information in one modality is used to focus processing in the other modality (a form of attention). This synergistic bi-modal operation improves surveillance efficacy by focusing auditory and visual "attention" on a particular target or location. Algorithms for focusing auditory and visual sensors using detection information were developed. These combination algorithms perform "zoom-with-enhanced-acuity" in both the visual and auditory domains, triggered by detection in either domain. Sensory-input processing algorithms focus on specific locations, indicated by at least one of the modalities. This spatially focused processing emulates biological attention-driven focusing. We showed that given information about the target, the acoustic algorithms were able to achieve over 80% correct target detection at signal-tonoise ratios (SNRs) of -20 dB and above, as compared with similar performance at SNRs of -10 db and above without target information from another modality. Similarly, the visual algorithm achieved performance of over 80% detection with added noise variance of 0.001 without target indication, but maintained 100% detection at added noise variance of 0.05 when acoustic target information was taken into account.

  11. Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

    Science.gov (United States)

    Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

    2015-05-01

    This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

  12. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.

  13. Robot path Planning Using  SIFT and Sonar Sensor Fusion

    DEFF Research Database (Denmark)

    Plascencia, Alfredo; Raposo, Hector

    2007-01-01

    This paper presents a novel map building approach for path planning purposes, which takes into account the uncertainty inherent in sensor measurements. To this end, Bayesian estimation and Dempster-Shafer evidential theory are used to fuse the sensory information and to update the occupancy...... and evidential grid maps, respectively. The approach is illustrated using actual measurements from a laboratory robot. The sensory information is obtained from a sonar array and the Scale Invariant Feature Transform (SIFT) algorithm. Finally, the resulting two evidential maps based on Bayes and Dempster theories...... are used for path planning using the potential field method. Both yield satisfying results...

  14. Autonomous underwater vehicle motion tracking using a Kalman filter for sensor fusion

    CSIR Research Space (South Africa)

    Holtzhausen, S

    2008-11-01

    Full Text Available to be discussed is the IMU. This sensor system consists of three accelerometers and three gyros. The accelerometers are positioned on the X, Y and Z axes and measure acceleration in their respective directions. 15th International conference on Mechatronics... of the vehicle. The accelerometer data can be used to determine linear motion by calculating the double integral of the accelerometer data over time. This will give the calculated movement of the vehicle in the given direction. The accelerometer data can also...

  15. Microwave and camera sensor fusion for the shape extraction of metallic 3D space objects

    Science.gov (United States)

    Shaw, Scott W.; Defigueiredo, Rui J. P.; Krishen, Kumar

    1989-01-01

    The vacuum of space presents special problems for optical image sensors. Metallic objects in this environment can produce intense specular reflections and deep shadows. By combining the polarized RCS with an incomplete camera image, it has become possible to better determine the shape of some simple three-dimensional objects. The radar data are used in an iterative procedure that generates successive approximations to the target shape by minimizing the error between computed scattering cross-sections and the observed radar returns. Favorable results have been obtained for simulations and experiments reconstructing plates, ellipsoids, and arbitrary surfaces.

  16. Coupled sensor/platform control design for low-level chemical detection with position-adaptive micro-UAVs

    Science.gov (United States)

    Goodwin, Thomas; Carr, Ryan; Mitra, Atindra K.; Selmic, Rastko R.

    2009-05-01

    We discuss the development of Position-Adaptive Sensors [1] for purposes for detecting embedded chemical substances in challenging environments. This concept is a generalization of patented Position-Adaptive Radar Concepts developed at AFRL for challenging conditions such as urban environments. For purposes of investigating the detection of chemical substances using multiple MAV (Micro-UAV) platforms, we have designed and implemented an experimental testbed with sample structures such as wooden carts that contain controlled leakage points. Under this general concept, some of the members of a MAV swarm can serve as external position-adaptive "transmitters" by blowing air over the cart and some of the members of a MAV swarm can serve as external position-adaptive "receivers" that are equipped with chemical or biological (chem/bio) sensors that function as "electronic noses". The objective can be defined as improving the particle count of chem/bio concentrations that impinge on a MAV-based position-adaptive sensor that surrounds a chemical repository, such as a cart, via the development of intelligent position-adaptive control algorithms. The overall effect is to improve the detection and false-alarm statistics of the overall system. Within the major sections of this paper, we discuss a number of different aspects of developing our initial MAV-Based Sensor Testbed. This testbed includes blowers to simulate position-adaptive excitations and a MAV from Draganfly Innovations Inc. with stable design modifications to accommodate our chem/bio sensor boom design. We include details with respect to several critical phases of the development effort including development of the wireless sensor network and experimental apparatus, development of the stable sensor boom for the MAV, integration of chem/bio sensors and sensor node onto the MAV and boom, development of position-adaptive control algorithms and initial tests at IDCAST (Institute for the Development and

  17. Multivariable Regression and Adaptive Neurofuzzy Inference System Predictions of Ash Fusion Temperatures Using Ash Chemical Composition of US Coals

    Directory of Open Access Journals (Sweden)

    Shahab Karimi

    2014-01-01

    Full Text Available In this study, the effects of ratios of dolomite, base/acid, silica, SiO2/Al2O3, and Fe2O3/CaO, base and acid oxides, and 11 oxides (SiO2, Al2O3, CaO, MgO, MnO, Na2O, K2O, Fe2O3, TiO2, P2O5, and SO3 on ash fusion temperatures for 1040 US coal samples from 12 states were evaluated using regression and adaptive neurofuzzy inference system (ANFIS methods. Different combinations of independent variables were examined to predict ash fusion temperatures in the multivariable procedure. The combination of the “11 oxides + (Base/Acid + Silica ratio” was the best predictor. Correlation coefficients (R2 of 0.891, 0.917, and 0.94 were achieved using nonlinear equations for the prediction of initial deformation temperature (IDT, softening temperature (ST, and fluid temperature (FT, respectively. The mentioned “best predictor” was used as input to the ANFIS system as well, and the correlation coefficients (R2 of the prediction were enhanced to 0.97, 0.98, and 0.99 for IDT, ST, and FT, respectively. The prediction precision that was achieved in this work exceeded that reported in previously published works.

  18. Energy-Aware Adaptive Cooperative FEC Protocol in MIMO Channel for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yong Jin

    2013-01-01

    Full Text Available We propose an adaptive cooperative forward error correction (ACFEC based on energy efficiency combining Reed-Solomon (RS coder algorithm and multiple input multiple output (MIMO channel technology with monitoring signal-to-noise ratio (SNR in wireless sensor networks. First, we propose a new Markov chain model for FEC based on RS codes and derive the expressions for QoS on the basis of this model, which comprise four metrics: throughput, packet error rate, delay, and energy efficiency. Then, we apply RS codes with the MIMO channel technology to the cross-layer design. Numerical and simulation results show that the joint design of MIMO and adaptive cooperative FEC based on RS codes can achieve considerable spectral efficiency gain, real-time performance, reliability, and energy utility.

  19. Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems

    CERN Document Server

    Basden, A G; Bharmal, N A; Bitenc, U; Brangier, M; Buey, T; Butterley, T; Cano, D; Chemla, F; Clark, P; Cohen, M; Conan, J -M; de Cos, F J; Dickson, C; Dipper, N A; Dunlop, C N; Feautrier, P; Fusco, T; Gach, J L; Gendron, E; Geng, D; Goodsell, S J; Gratadour, D; Greenaway, A H; Guesalaga, A; Guzman, C D; Henry, D; Holck, D; Hubert, Z; Huet, J M; Kellerer, A; Kulcsar, C; Laporte, P; Roux, B Le; Looker, N; Longmore, A J; Marteaud, M; Martin, O; Meimon, S; Morel, C; Morris, T J; Myers, R M; Osborn, J; Perret, D; Petit, C; Raynaud, H; Reeves, A P; Rousset, G; Lasheras, F Sanchez; Rodriguez, M Sanchez; Santos, J D; Sevin, A; Sivo, G; Stadler, E; Stobie, B; Talbot, G; Todd, S; Vidal, F; Younger, E J

    2016-01-01

    Recent advances in adaptive optics (AO) have led to the implementation of wide field-of-view AO systems. A number of wide-field AO systems are also planned for the forthcoming Extremely Large Telescopes. Such systems have multiple wavefront sensors of different types, and usually multiple deformable mirrors (DMs). Here, we report on our experience integrating cameras and DMs with the real-time control systems of two wide-field AO systems. These are CANARY, which has been operating on-sky since 2010, and DRAGON, which is a laboratory adaptive optics real-time demonstrator instrument. We detail the issues and difficulties that arose, along with the solutions we developed. We also provide recommendations for consideration when developing future wide-field AO systems.

  20. Performance simulation of the ERIS pyramid wavefront sensor module in the VLT adaptive optics facility

    Science.gov (United States)

    Quirós-Pacheco, Fernando; Agapito, Guido; Riccardi, Armando; Esposito, Simone; Le Louarn, Miska; Marchetti, Enrico

    2012-07-01

    This paper presents the performance analysis based on numerical simulations of the Pyramid Wavefront sensor Module (PWM) to be included in ERIS, the new Adaptive Optics (AO) instrument for the Adaptive Optics Facility (AOF). We have analyzed the performance of the PWM working either in a low-order or in a high-order wavefront sensing mode of operation. We show that the PWM in the high-order sensing mode can provide SR > 90% in K band using bright guide stars under median seeing conditions (0.85 arcsec seeing and 15 m/s of wind speed). In the low-order sensing mode, the PWM can sense and correct Tip-Tilt (and if requested also Focus mode) with the precision required to assist the LGS observations to get an SR > 60% and > 20% in K band, using up to a ~16.5 and ~19.5 R-magnitude guide star, respectively.