ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS

OpenAIRE

W. Nakanishi; T. Fuse; T. Ishikawa

2015-01-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation ...

Kalman filters for real-time magnetic island phase tracking

International Nuclear Information System (INIS)

Borgers, D.P.; Lauret, M.; Baar, M.R. de

2013-01-01

Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade

Kalman filters for real-time magnetic island phase tracking

Energy Technology Data Exchange (ETDEWEB)

Borgers, D.P. [Hybrid and Networked Systems, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Lauret, M., E-mail: M.Lauret@tue.nl [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Baar, M.R. de [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands)

2013-11-15

Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade.

Correlation Filter Learning Toward Peak Strength for Visual Tracking.

Science.gov (United States)

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

Multisensor Distributed Track Fusion AlgorithmBased on Strong Tracking Filter and Feedback Integration1）

Institute of Scientific and Technical Information of China (English)

YANGGuo-Sheng; WENCheng-Lin; TANMin

2004-01-01

A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.

Visual object tracking by correlation filters and online learning

Science.gov (United States)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters

Directory of Open Access Journals (Sweden)

M. Sanjeev Arulampalam

2004-11-01

Full Text Available We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs. Three different (PFs are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS framework. The proposed filters are (i multiple model PF (MMPF, (ii auxiliary MMPF (AUX-MMPF, and (iii jump Markov system PF (JMS-PF. The performance of these filters is compared with that of standard interacting multiple model (IMM-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i single-sensor case, (ii multisensor case, and (iii tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem.

Direct and accelerated parameter mapping using the unscented Kalman filter.

Science.gov (United States)

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

Institute of Scientific and Technical Information of China (English)

XIAO Kun; FANG Shao-ji; PANG Yong-jie

2007-01-01

To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

Robotic fish tracking method based on suboptimal interval Kalman filter

Science.gov (United States)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Nonlinear Principal Component Analysis Using Strong Tracking Filter

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

Kalman Filter Tracking on Parallel Architectures

International Nuclear Information System (INIS)

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

2016-01-01

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment

Tracking of multiple objects with time-adjustable composite correlation filters

Science.gov (United States)

Ruchay, Alexey; Kober, Vitaly; Chernoskulov, Ilya

2017-09-01

An algorithm for tracking of multiple objects in video based on time-adjustable adaptive composite correlation filtering is proposed. For each frame a bank of composite correlation filters are designed in such a manner to provide invariance to pose, occlusion, clutter, and illumination changes. The filters are synthesized with the help of an iterative algorithm, which optimizes the discrimination capability for each object. The filters are adapted to the objects changes online using information from the current and past scene frames. Results obtained with the proposed algorithm using real-life scenes are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.

Detecting Power Voltage Dips using Tracking Filters - A Comparison against Kalman

Directory of Open Access Journals (Sweden)

STANCIU, I.-R.

2012-11-01

Full Text Available Due of its significant economical impact, Power-Quality (PQ analysis is an important domain today. Severe voltage distortions affect the consumers and disturb their activity. They may be caused by short circuits (in this case the voltage drops significantly or by varying loads (with a smaller drop. These two types are the PQ currently issues. Monitoring these phenomena (called dips or sags require powerful techniques. Digital Signal Processing (DSP algorithms are currently employed to fulfill this task. Discrete Wavelet Transforms, (and variants, Kalman filters, and S-Transform are currently proposed by researchers to detect voltage dips. This paper introduces and examines a new tool to detect voltage dips: the so-called tracking filters. Discovered and tested during the cold war, they can estimate a parameter of interest one-step-ahead based on the previously observed values. Two filters are implemented. Their performance is assessed by comparison against the Kalman filter?s results.

Further studies on the filtration of liquids using Kapton nuclear track micro filters

International Nuclear Information System (INIS)

Guo, S.L.; Ganz, M.; Fuest, M.; Vater, P.; Brandt, R.

1990-01-01

The flow rate of some liquids (water, heptane, toluene, xylene, dodecane) through Kapton nuclear track filters has been measured. The results can be interpreted with a modified Poiseuille formula. The influence of the viscosity of the liquids on their throughput through Kapton nuclear track filters has been determined. The purification of liquids with Kapton filters has been investigated. It is possible to measure the density of solid particles in deionized water by using Kapton filters. Consequently, nuclear track filters can remove solid particles from high-purity liquids. The soaking effect of some common liquid chemicals on Kapton filters has also been studied, no such soaking could be observed in most of the cases. Finally, these Kapton nuclear track filters are compared with a filter device from Hamamatsu (Japan). (orig.) [de

Track filter on the basis of a cellular automation

International Nuclear Information System (INIS)

Glazov, A.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.

1991-01-01

The filtering method for tracks in discrete detectors based on the cellular automation is described. Results of the application of this method to experimental data (the spectrometer ARES) are quite successful: threefold reduction of input information with data grouping according to their belonging to separate tracks. They lift up percentage of useful events, which simplifies and accelerates considerably their next recognition. The described cellular automation for track filtering can be successfully applied in parallel computers and also in on-line mode if hardware implementation is used. 21 refs.; 11 figs

PARTICLE FILTER BASED VEHICLE TRACKING APPROACH WITH IMPROVED RESAMPLING STAGE

Directory of Open Access Journals (Sweden)

Wei Leong Khong

2014-02-01

Full Text Available Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehicle appearance and illumination, besides the occlusion and overlapping incidents. Particle filter has been proven as an approach which can overcome nonlinear and non-Gaussian situations caused by cluttered background and occlusion incidents. Unfortunately, conventional particle filter approach encounters particle degeneracy especially during and after the occlusion. Particle filter with sampling important resampling (SIR is an important step to overcome the drawback of particle filter, but SIR faced the problem of sample impoverishment when heavy particles are statistically selected many times. In this work, genetic algorithm has been proposed to be implemented in the particle filter resampling stage, where the estimated position can converge faster to hit the real position of target vehicle under various occlusion incidents. The experimental results show that the improved particle filter with genetic algorithm resampling method manages to increase the tracking accuracy and meanwhile reduce the particle sample size in the resampling stage.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Energy awareness for supercapacitors using Kalman filter state-of-charge tracking

Science.gov (United States)

Nadeau, Andrew; Hassanalieragh, Moeen; Sharma, Gaurav; Soyata, Tolga

2015-11-01

Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-mean-square error for predicting usable buffered energy.

Realization of nuclear track filters and their applications to the study of environmental aerosol samples

International Nuclear Information System (INIS)

Guo Shilun

1993-01-01

Detailed study of the behaviours of radon decay products requires the knowledge of environmental aerosols. A combination of devices and instruments have been tested to be superior in study of aerosol characteristics. Nuclear track filters and cascade track filter impactor are basic devices, which bring the functions of α -spectrometer, scanning electron microscope and electron microprobe into full play. This paper describes how to use these devices and instruments to collect aerosols, to determine aerosol radioactivity, size distribution and elemental compositions and to determine the concentration of aerosols in air as a function of aerosol sizes, which are important parameters in dominating the interactions between aerosol and radon decay products. (author). 15 refs, 7 figs

LHCb: Alignment of the LHCb Detector with Kalman Filter Fitted Tracks

CERN Multimedia

Amoraal, J; Hulsbergen, W; Needham, M; Nicolas, L; Pozzi, S; Raven, G; Vecchi, S

2009-01-01

We report on an implementation of a global chisquare algorithm for the simultaneous alignment of all tracking systems in the LHCb detector. Our algorithm uses hit residuals from the standard LHCb track fit which is based on a Kalman filter. The algorithm is implemented in the LHCb reconstruction framework and exploits the fact that all sensitive detector elements have the same geometry interface. A vertex constraint is implemented by fitting tracks to a common point and propagating the change in track parameters to the hit residuals. To remove unconstrained or poorly constrained degrees of freedom (so-called weak modes) the average movements of (subsets of) alignable detector elements can be fixed with Lagrange constraints. Alternatively, weak modes can be removed with a cutoff in the eigenvalue spectrum of the second derivative of the chisquare. As for all LHCb reconstruction and analysis software the configuration of the algorithm is done in python and gives detailed control over the selection of alignable ...

On tempo tracking: Tempogram representation and Kalman filtering

NARCIS (Netherlands)

Cemgil, A.T.; Kappen, H.J.; Desain, P.W.M.; Honing, H.J.

2001-01-01

We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of

Joint Conditional Random Field Filter for Multi-Object Tracking

Directory of Open Access Journals (Sweden)

Luo Ronghua

2011-03-01

Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.

UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data

Directory of Open Access Journals (Sweden)

Karen Uribe-Murcia

2018-01-01

Full Text Available In smart cities, vehicles tracking is organized to increase safety by localizing cars using the Global Positioning System (GPS. The GPS-based system provides accurate tracking but is also required to be reliable and robust. As a main estimator, we propose using the unbiased finite impulse response (UFIR filter, which meets these needs as being more robust than the Kalman filter (KF. The UFIR filter is developed for vehicle tracking in discrete-time state-space over wireless sensor networks (WSNs with time-stamped data discretely delayed on k-step-lags and missing data. The state-space model is represented in a way such that the UFIR filter, KF, and H∞ filter can be used universally. Applications are given for measurement data, which are cooperatively transferred from a vehicle to a central station through several nodes with k-step-lags. Better tracking performance of the UFIR filter is shown experimentally.

Tracking single dynamic MEG dipole sources using the projected Extended Kalman Filter.

Science.gov (United States)

Yao, Yuchen; Swindlehurst, A Lee

2011-01-01

This paper presents two new algorithms based on the Extended Kalman Filter (EKF) for tracking the parameters of single dynamic magnetoencephalography (MEG) dipole sources. We assume a dynamic MEG dipole source with possibly both time-varying location and dipole orientation. The standard EKF-based tracking algorithm performs well under the assumption that the dipole source components vary in time as a Gauss-Markov process, provided that the background noise is temporally stationary. We propose a Projected-EKF algorithm that is adapted to a more forgiving condition where the background noise is temporally nonstationary, as well as a Projected-GLS-EKF algorithm that works even more universally, when the dipole components vary arbitrarily from one sample to the next.

Using Gaussian Process Annealing Particle Filter for 3D Human Tracking

Directory of Open Access Journals (Sweden)

Michael Rudzsky

2008-01-01

Full Text Available We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loss.

Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

Directory of Open Access Journals (Sweden)

Jing Liu

2012-01-01

Full Text Available The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.

Science.gov (United States)

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J

2017-03-03

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Estimation of three-dimensional radar tracking using modified extended kalman filter

Science.gov (United States)

Aditya, Prima; Apriliani, Erna; Khusnul Arif, Didik; Baihaqi, Komar

2018-03-01

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Directory of Open Access Journals (Sweden)

Anthony Hoak

2017-03-01

Full Text Available We develop an interactive likelihood (ILH for sequential Monte Carlo (SMC methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL and TUD-Stadtmitte using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA and classification of events, activities and relationships for multi-object trackers (CLEAR MOT. In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Dual linear structured support vector machine tracking method via scale correlation filter

Science.gov (United States)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

Khalid, Syed Safwan; Abrar, Shafayat

2017-01-01

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

A low-complexity interacting multiple model filter for maneuvering target tracking

KAUST Repository

Khalid, Syed Safwan

2017-01-22

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

INFLUENCE OF STOCHASTIC NOISE STATISTICS ON KALMAN FILTER PERFORMANCE BASED ON VIDEO TARGET TRACKING

Institute of Scientific and Technical Information of China (English)

Chen Ken; Napolitano; Zhang Yun; Li Dong

2010-01-01

The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research,efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degradation in the application of video tracking.

Group Targets Tracking Using Multiple Models GGIW-CPHD Based on Best-Fitting Gaussian Approximation and Strong Tracking Filter

Directory of Open Access Journals (Sweden)

Yun Wang

2016-01-01

Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

Science.gov (United States)

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-11-18

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration-which are the basis of tracking error estimation-are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (-0.25 cycle, 0.25 cycle) to (-0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio

Selection of noise parameters for Kalman filter

Institute of Scientific and Technical Information of China (English)

Ka-Veng Yuen; Ka-In Hoi; Kai-Meng Mok

2007-01-01

The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.

Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

Science.gov (United States)

Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

2017-11-01

Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

Fish tracking by combining motion based segmentation and particle filtering

Science.gov (United States)

Bichot, E.; Mascarilla, L.; Courtellemont, P.

2006-01-01

In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.

Interacting Multiple Model (IMM Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Directory of Open Access Journals (Sweden)

Hua Liu

2017-06-01

Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

Science.gov (United States)

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

Science.gov (United States)

Feng, Kaiqiang; Li, Jie; Zhang, Xi; Zhang, Xiaoming; Shen, Chong; Cao, Huiliang; Yang, Yanyu; Liu, Jun

2018-06-12

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

Science.gov (United States)

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Particle filters for object tracking: enhanced algorithm and efficient implementations

International Nuclear Information System (INIS)

Abd El-Halym, H.A.

2010-01-01

Object tracking and recognition is a hot research topic. In spite of the extensive research efforts expended, the development of a robust and efficient object tracking algorithm remains unsolved due to the inherent difficulty of the tracking problem. Particle filters (PFs) were recently introduced as a powerful, post-Kalman filter, estimation tool that provides a general framework for estimation of nonlinear/ non-Gaussian dynamic systems. Particle filters were advanced for building robust object trackers capable of operation under severe conditions (small image size, noisy background, occlusions, fast object maneuvers ..etc.). The heavy computational load of the particle filter remains a major obstacle towards its wide use.In this thesis, an Excitation Particle Filter (EPF) is introduced for object tracking. A new likelihood model is proposed. It depends on multiple functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modified the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. This modification is based on re-exciting the particles if their weights fall below a memorized weight value. The proposed enhanced PF is implemented in software and evaluated. Its results are compared with a single likelihood function PF tracker, Particle Swarm Optimization (PSO) tracker, a correlation tracker, as well as, an edge tracker. The experimental results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness, and occlusion compared with other methods Efficient novel hardware architectures of the Sample Important Re sample Filter (SIRF) and the EPF are implemented. Three novel hardware architectures of the SIRF for object tracking are introduced. The first architecture is a two-step sequential PF machine, where particle generation, weight calculation and normalization are carried out in parallel during the first step followed by a sequential re

An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters

KAUST Repository

Ait-El-Fquih, Boujemaa

2017-12-11

This work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters\\' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters\\' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters\\' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles\\' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.

An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters

KAUST Repository

Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2017-01-01

This work addresses the state-parameter filtering problem for dynamical systems with relatively large-dimensional state and low-dimensional parameters' vector. A Bayesian filtering algorithm combining the strengths of the particle filter (PF) and the ensemble Kalman filter (EnKF) is proposed. At each assimilation cycle of the proposed EnKF-PF, the PF is first used to sample the parameters' ensemble followed by the EnKF to compute the state ensemble conditional on the resulting parameters' ensemble. The proposed scheme is expected to be more efficient than the traditional state augmentation techniques, which suffer from the curse of dimensionality and inconsistency that is particularly pronounced when the state is a strongly nonlinear function of the parameters. In the new scheme, the EnKF and PF interact via their ensembles' members, in contrast with the recently introduced two-stage EnKF-PF (TS-EnKF-PF), which exchanges point estimates between EnKF and PF while requiring almost double the computational load. Numerical experiments are conducted with the Lorenz-96 model to assess the behavior of the proposed filter and to evaluate its performances against the joint PF, joint EnKF, and TS-EnKF-PF. Numerical results suggest that the EnKF-PF performs best in all tested scenarios. It was further found to be more robust, successfully estimating both state and parameters in different sensitivity experiments.

Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

Institute of Scientific and Technical Information of China (English)

Changyun Liu; Penglang Shui; Gang Wei; Song Li

2014-01-01

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneu-vers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is pre-sented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter (UKF) via predicted variance Pk|k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneu-vering target compared with the standard UKF.

Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

Directory of Open Access Journals (Sweden)

Hamza Benzerrouk

2018-03-01

Full Text Available Multi-Unmanned Aerial Vehicle (UAV Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss–Hermite information filter, as well as a seventh-degree cubature information filter (CIF, is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs. A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters.

Signal Tracking Beyond the Time Resolution of an Atomic Sensor by Kalman Filtering

Science.gov (United States)

Jiménez-Martínez, Ricardo; Kołodyński, Jan; Troullinou, Charikleia; Lucivero, Vito Giovanni; Kong, Jia; Mitchell, Morgan W.

2018-01-01

We study causal waveform estimation (tracking) of time-varying signals in a paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation probing. We use Kalman filtering, which optimally tracks known linear Gaussian stochastic processes, to estimate stochastic input signals that we generate by optical pumping. Comparing the known input to the estimates, we confirm the accuracy of the atomic statistical model and the reliability of the Kalman filter, allowing recovery of waveform details far briefer than the sensor's intrinsic time resolution. With proper filter choice, we obtain similar benefits when tracking partially known and non-Gaussian signal processes, as are found in most practical sensing applications. The method evades the trade-off between sensitivity and time resolution in coherent sensing.

An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

Directory of Open Access Journals (Sweden)

Mingyue Cui

2016-09-01

Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.

3D head pose estimation and tracking using particle filtering and ICP algorithm

KAUST Repository

Ben Ghorbel, Mahdi; Baklouti, Malek; Couvet, Serge

2010-01-01

This paper addresses the issue of 3D head pose estimation and tracking. Existing approaches generally need huge database, training procedure, manual initialization or use face feature extraction manually extracted. We propose a framework for estimating the 3D head pose in its fine level and tracking it continuously across multiple Degrees of Freedom (DOF) based on ICP and particle filtering. We propose to approach the problem, using 3D computational techniques, by aligning a face model to the 3D dense estimation computed by a stereo vision method, and propose a particle filter algorithm to refine and track the posteriori estimate of the position of the face. This work comes with two contributions: the first concerns the alignment part where we propose an extended ICP algorithm using an anisotropic scale transformation. The second contribution concerns the tracking part. We propose the use of the particle filtering algorithm and propose to constrain the search space using ICP algorithm in the propagation step. The results show that the system is able to fit and track the head properly, and keeps accurate the results on new individuals without a manual adaptation or training. © Springer-Verlag Berlin Heidelberg 2010.

GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation

Directory of Open Access Journals (Sweden)

Jinguang Chen

2015-01-01

Full Text Available For the standard Gaussian mixture probability hypothesis density (GM-PHD filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.

Particle Filter-Based Target Tracking Algorithm for Magnetic Resonance-Guided Respiratory Compensation : Robustness and Accuracy Assessment

NARCIS (Netherlands)

Bourque, Alexandra E; Bedwani, Stéphane; Carrier, Jean-François; Ménard, Cynthia; Borman, Pim; Bos, Clemens; Raaymakers, Bas W; Mickevicius, Nikolai; Paulson, Eric; Tijssen, Rob H N

PURPOSE: To assess overall robustness and accuracy of a modified particle filter-based tracking algorithm for magnetic resonance (MR)-guided radiation therapy treatments. METHODS AND MATERIALS: An improved particle filter-based tracking algorithm was implemented, which used a normalized

The Sensitivity of the Input Impedance Parameters of Track Circuits to Changes in the Parameters of the Track

Directory of Open Access Journals (Sweden)

Lubomir Ivanek

2017-01-01

Full Text Available This paper deals with the sensitivity of the input impedance of an open track circuit in the event that the parameters of the track are changed. Weather conditions and the state of pollution are the most common reasons for parameter changes. The results were obtained from the measured values of the parameters R (resistance, G (conductance, L (inductance, and C (capacitance of a rail superstructure depending on the frequency. Measurements were performed on a railway siding in Orlova. The results are used to design a predictor of occupancy of a track section. In particular, we were interested in the frequencies of 75 and 275 Hz for this purpose. Many parameter values of track substructures have already been solved in different works in literature. At first, we had planned to use the parameter values from these sources when we designed the predictor. Deviations between them, however, are large and often differ by three orders of magnitude (see Tab.8. From this perspective, this article presents data that have been updated using modern measurement devices and computer technology. And above all, it shows a transmission (cascade matrix used to determine the parameters.

The research of radar target tracking observed information linear filter method

Science.gov (United States)

Chen, Zheng; Zhao, Xuanzhi; Zhang, Wen

2018-05-01

Aiming at the problems of low precision or even precision divergent is caused by nonlinear observation equation in radar target tracking, a new filtering algorithm is proposed in this paper. In this algorithm, local linearization is carried out on the observed data of the distance and angle respectively. Then the kalman filter is performed on the linearized data. After getting filtered data, a mapping operation will provide the posteriori estimation of target state. A large number of simulation results show that this algorithm can solve above problems effectively, and performance is better than the traditional filtering algorithm for nonlinear dynamic systems.

Passive Target Tracking in Non-cooperative Radar System Based on Particle Filtering

Institute of Scientific and Technical Information of China (English)

LI Shuo; TAO Ran

2006-01-01

We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.

Robust filtering for uncertain systems a parameter-dependent approach

CERN Document Server

Gao, Huijun

2014-01-01

This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties, and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and H¥ filtering, are addressed, and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features: ·        design approaches to robust filters arranged according to varying complexity level, and emphasizing robust filtering in the parameter-dependent framework for the first time; ·...

Kalman Filter Based Tracking in an Video Surveillance System

Directory of Open Access Journals (Sweden)

SULIMAN, C.

2010-05-01

Full Text Available In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtained from the Kalman filter, and we show the correct functionality of the Kalman filter based tracking. The tests were performed using video data taken with the help of a fix camera. The tested algorithm has shown promising results.

Treatment of non-Gaussian tails of multiple Coulomb scattering in track fitting with a Gaussian-sum filter

International Nuclear Information System (INIS)

Strandlie, A.; Wroldsen, J.

2006-01-01

If any of the probability densities involved in track fitting deviate from the Gaussian assumption, it is plausible that a non-linear estimator which better takes the actual shape of the distribution into account can do better. One such non-linear estimator is the Gaussian-sum filter, which is adequate if the distributions under consideration can be approximated by Gaussian mixtures. The main purpose of this paper is to present a Gaussian-sum filter for track fitting, based on a two-component approximation of the distribution of angular deflections due to multiple scattering. In a simulation study within a linear track model the Gaussian-sum filter is shown to be a competitive alternative to the Kalman filter. Scenarios at various momenta and with various maximum number of components in the Gaussian-sum filter are considered. Particularly at low momenta the Gaussian-sum filter yields a better estimate of the uncertainties than the Kalman filter, and it is also slightly more precise than the latter

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

Matthies, Hermann G.

2016-11-25

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.

Bayesian Parameter Estimation via Filtering and Functional Approximations

KAUST Repository

Matthies, Hermann G.; Litvinenko, Alexander; Rosic, Bojana V.; Zander, Elmar

2016-01-01

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.

Chaotic secure communication based on strong tracking filtering

International Nuclear Information System (INIS)

Li Xiongjie; Xu Zhengguo; Zhou Donghua

2008-01-01

A scheme for implementing secure communication based on chaotic maps and strong tracking filter (STF) is presented, and a modified STF algorithm with message estimation is developed for the special requirement of chaotic secure communication. At the emitter, the message symbol is modulated by chaotic mapping and is output through a nonlinear function. At the receiver, the driving signal is received and the message symbol is recovered dynamically by the STF with estimation of message symbol. Simulation results of Holmes map demonstrate that when message symbols are binary codes, STF can effectively recover the codes of the message from the noisy chaotic signals. Compared with the extended Kalman filter (EKF), STF has a lower bit error rate

Context-Aware Correlation Filter Tracking

KAUST Repository

Mueller, Matthias; Smith, Neil; Ghanem, Bernard

2017-01-01

Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.

Context-Aware Correlation Filter Tracking

KAUST Repository

Mueller, Matthias

2017-11-09

Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.

Kalman filter data assimilation: targeting observations and parameter estimation.

Science.gov (United States)

Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

2014-06-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Kalman filter data assimilation: Targeting observations and parameter estimation

International Nuclear Information System (INIS)

Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

2014-01-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

Science.gov (United States)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Preparation of Track Etch Membrane Filters Using Polystyrene Film

International Nuclear Information System (INIS)

Kaewsaenee, Jerawut; Ratanatongchai, Wichian; Supaphol, Pitt; Visal-athaphand, Pinpan

2007-08-01

Full text: Polystyrene nuclear track etch membrane filters was prepared by exposed 13 .m thin film polystyrene with fission fragment. Nuclear latent track was enlarged to through hole on the film by etching with 80 o C 40% H 2 SO 4 with K 2 Cr 2 O 7 solution for 6-10 hour. The hole size was depend on concentration of etching solution and etching time with 1.3-3.4 .m hole diameter. The flow rate test of water was 0.79-1.56 mm cm-2 min-1 at 109.8-113.7 kPa pressure

Ion track etching revisited: I. Correlations between track parameters in aged polymers

Science.gov (United States)

Fink, D.; Muñoz H., G.; García A., H.; Vacik, J.; Hnatowicz, V.; Kiv, A.; Alfonta, L.

2018-04-01

Some yet poorly understood problems of etching of pristine and swift heavy ion track-irradiated aged polymers were treated, by applying conductometry across the irradiated foils during etching. The onset times of etchant penetration across pristine foils, and the onset times of the different etched track regimes in irradiated foils were determined for polymers of various proveniences, fluences and ages, as well as their corresponding etching speeds. From the results, correlations of the parameters with each other were deduced. The normalization of these parameters enables one to compare irradiated polymer foils of different origin and treatment with one another. In a number of cases, also polymeric gel formation and swelling occur which influence the track etching behaviour. The polymer degradation during aging influences the track etching parameters, which differ from each other on both sides of the foils. With increasing sample age, these differences increase.

Identification of lead acid battery parameters using kalman filtering in photovoltaic system

International Nuclear Information System (INIS)

Boutte, Aissa

2006-01-01

The conventional methods of battery identification parameters consist in estimating the state of charge (SOC), and in establishing a command adapted to charge or to discharge the battery, based on electrical model developed with fixed parameters, These methods are inefficient. The causes of this ineffectiveness are different: In the first place model does not adapt itself with the battery (fixed parameters, lack of modulated parameters, a big non-linearity ...).Secondly, the impossibility for the developed algorithms, to adapt itself with the change of the battery's parameters. New models of identification are used by combining the conventional methods with adaptive and dynamic techniques. They already used in other domains where they have proved a good efficiency and a robustness. Taking into consideration the problems mentioned, and trying to resolve them, we have chosen among the various methods of estimation, Kalman filter (KF) known for its efficiency, in the field of tracking parameters. In this work we try tp represent new ideas, to identify battery parameters using KF method and make an experimental analysis of the performance of this method by using Lead Acid Battery, which is a part of a photovoltaic system (PV).(Author)

Kalman filter tracking on parallel architectures

Science.gov (United States)

Cerati, G.; Elmer, P.; Krutelyov, S.; Lantz, S.; Lefebvre, M.; McDermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Wurthwein, F.; Yagil, A.

2017-10-01

We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.

Application of Evolution Strategies to the Design of Tracking Filters with a Large Number of Specifications

Directory of Open Access Journals (Sweden)

Jesús García Herrero

2003-07-01

Full Text Available This paper describes the application of evolution strategies to the design of interacting multiple model (IMM tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The design problem is stated as a numeric search in the filter parameters space to attain all specifications or at least minimize, in a compromise, the excess over some specifications as much as possible, applying global optimization techniques coming from evolutionary computation field. Besides, a new methodology is proposed to integrate specifications in a fitness function able to effectively guide the search to suitable solutions. The method has been applied to the design of an IMM tracker for a real-world civil air traffic control application: the accomplishment of specifications defined for the future European ARTAS system.

Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

Frequency tracking and variable bandwidth for line noise filtering without a reference.

Science.gov (United States)

Kelly, John W; Collinger, Jennifer L; Degenhart, Alan D; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

2011-01-01

This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures

Energy Technology Data Exchange (ETDEWEB)

Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U., Phys. Dept.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U., Phys. Dept.; Riley, Daniel [Cornell U., Phys. Dept.; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U., Phys. Dept.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego

2017-11-16

Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Examples include the Intel Xeon Phi, GPGPUs, and similar technologies. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.

Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

Science.gov (United States)

Qin, Yong; Ma, Hong; Chen, Jinfeng; Cheng, Li

2015-12-01

Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

Blind Source Parameters for Performance Evaluation of Despeckling Filters

Directory of Open Access Journals (Sweden)

Nagashettappa Biradar

2016-01-01

Full Text Available The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI, and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink embedded with Stein’s unbiased risk estimation (SURE. The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.

Blind Source Parameters for Performance Evaluation of Despeckling Filters.

Science.gov (United States)

Biradar, Nagashettappa; Dewal, M L; Rohit, ManojKumar; Gowre, Sanjaykumar; Gundge, Yogesh

2016-01-01

The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein's unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.

Traditional Tracking with Kalman Filter on Parallel Architectures

Science.gov (United States)

Cerati, Giuseppe; Elmer, Peter; Lantz, Steven; MacNeill, Ian; McDermott, Kevin; Riley, Dan; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

2015-05-01

Power density constraints are limiting the performance improvements of modern CPUs. To address this, we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.

A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

Science.gov (United States)

Liu, Zhuowei; Chen, Shuxin; Wu, Hao; He, Renke; Hao, Lin

2018-01-01

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. PMID:29617348

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

Science.gov (United States)

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; Masciovecchio, Mario; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

2017-08-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

Directory of Open Access Journals (Sweden)

Cerati Giuseppe

2017-01-01

Full Text Available For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU, ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC, for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

Energy Technology Data Exchange (ETDEWEB)

Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U.; Riley, Daniel [Cornell U., LNS; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego

2017-01-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

Track Reconstruction in the ATLAS Experiment The Deterministic Annealing Filter

CERN Document Server

Fleischmann, S

2006-01-01

The reconstruction of the trajectories of charged particles is essential for experiments at the LHC. The experiments contain precise tracking systems structured in layers around the collision point which measure the positions where particle trajectories intersect those layers. The physics analysis on the other hand mainly needs the momentum and direction of the particle at the estimated creation or reaction point. It is therefore needed to determine these parameters from the initial measurements. At the LHC one has to deal with high backgrounds while even small deficits or artifacts can reduce the signal or may produce additional background after event selection. The track reconstruction does not only contain the estimation of the track parameters, but also a pattern recognition deciding which measurements belong to a track and how many particle tracks can be found. Track reconstruction at the ATLAS experiment suffers from the high event rate at the LHC resulting in a high occupancy of the tracking devices. A...

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

Science.gov (United States)

Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun

2016-05-09

The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

A RSSI-based parameter tracking strategy for constrained position localization

Science.gov (United States)

Du, Jinze; Diouris, Jean-François; Wang, Yide

2017-12-01

In this paper, a received signal strength indicator (RSSI)-based parameter tracking strategy for constrained position localization is proposed. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. In the context of applications where the positions are constrained on a grid, a novel tracking strategy is proposed to determine the real position and obtain the actual parameters in the monitored region. Based on practical data acquired from a real localization system, an experimental channel model is constructed to provide RSSI values and verify the proposed tracking strategy. Quantitative criteria are given to guarantee the efficiency of the proposed tracking strategy by providing a trade-off between the grid resolution and parameter variation. The simulation results show a good behavior of the proposed tracking strategy in the presence of space-time variation of the propagation channel. Compared with the existing RSSI-based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. In addition, a tracking test is performed to validate the effectiveness of the proposed tracking strategy.

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.

Science.gov (United States)

Li, Jian; Wei, Xinguo; Zhang, Guangjun

2017-08-21

Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method.

Kalman filter estimation of RLC parameters for UMP transmission line

Directory of Open Access Journals (Sweden)

Mohd Amin Siti Nur Aishah

2018-01-01

Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

Evaluation of an image-based tracking workflow with Kalman filtering for automatic image plane alignment in interventional MRI.

Science.gov (United States)

Neumann, M; Cuvillon, L; Breton, E; de Matheli, M

2013-01-01

Recently, a workflow for magnetic resonance (MR) image plane alignment based on tracking in real-time MR images was introduced. The workflow is based on a tracking device composed of 2 resonant micro-coils and a passive marker, and allows for tracking of the passive marker in clinical real-time images and automatic (re-)initialization using the microcoils. As the Kalman filter has proven its benefit as an estimator and predictor, it is well suited for use in tracking applications. In this paper, a Kalman filter is integrated in the previously developed workflow in order to predict position and orientation of the tracking device. Measurement noise covariances of the Kalman filter are dynamically changed in order to take into account that, according to the image plane orientation, only a subset of the 3D pose components is available. The improved tracking performance of the Kalman extended workflow could be quantified in simulation results. Also, a first experiment in the MRI scanner was performed but without quantitative results yet.

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

Science.gov (United States)

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-11-11

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Target Response Adaptation for Correlation Filter Tracking

KAUST Repository

Bibi, Adel Aamer

2016-09-16

Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations to actual translations in the image, which become unreliable in many realistic tracking scenarios including fast motion, occlusion, etc. In these cases, the traditional use of a single centered Gaussian as the target response impedes tracker performance and can lead to unrecoverable drift. To circumvent this major drawback, we propose a generic framework that can adaptively change the target response from frame to frame, so that the tracker is less sensitive to the cases where circular shifts do not reliably approximate translations. To do that, we reformulate the underlying optimization to solve for both the filter and target response jointly, where the latter is regularized by measurements made using actual translations. This joint problem has a closed form solution and thus allows for multiple templates, kernels, and multi-dimensional features. Extensive experiments on the popular OTB100 benchmark show that our target adaptive framework can be combined with many CF trackers to realize significant overall performance improvement (ranging from 3 %-13.5% in precision and 3.2 %-13% in accuracy), especially in categories where this adaptation is necessary (e.g. fast motion, motion blur, etc.). © Springer International Publishing AG 2016.

Development of etched nuclear tracks

International Nuclear Information System (INIS)

Somogyi, G.

1980-01-01

The theoretical description of the evolution of etched tracks in solid state nuclear track detectors is considered for different initial conditions, for the cases of constant and varying track etch rates, isotropic and anisotropic bulk etching as well as for thick and thin detectors. It is summarized how one can calculate the main parameters of etch-pit geometry, the track length, the axes of a surface track opening, track profile and track contour. The application of the theory of etch-track evolution is demonstrated with selected practical problems. Attention is paid to certain questions related to the determination of unknown track parameters and calculation of surface track sizes. Finally, the theory is extended to the description of the perforation and etch-hole evolution process in thin detectors, which is of particular interest for track radiography and nuclear filter production. (orig.)

Development of etched nuclear tracks

International Nuclear Information System (INIS)

Somogyi, G.

1979-01-01

The theoretical description of the evolution of etched tracks in solid state nuclear track detectors is considered for different initial conditions, for the cases of constant and varying track etch rates, isotopic and unisotropic bulk etching as well as for thick and thin detectors. It is summarized how the main parameters of etch-pit geometry, the track length, the axes of a surface track opening, the track profile and the track contour can be calculated. The application of the theory of etch-track evolution is demonstrated with selected practical problems. Attention is paid to certain questions related to the determination of unknown track parameters and calculation of surface track sizes. Finally, the theory is extended to the description of the perforation and etch-hole evolution process in thin detectors, which is of particular interest for track radiography and nuclear filter production. (author)

Scheme of adaptive polarization filtering based on Kalman model

Institute of Scientific and Technical Information of China (English)

Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande

2006-01-01

A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.

Particle Filtering Applied to Musical Tempo Tracking

Directory of Open Access Journals (Sweden)

Macleod Malcolm D

2004-01-01

Full Text Available This paper explores the use of particle filters for beat tracking in musical audio examples. The aim is to estimate the time-varying tempo process and to find the time locations of beats, as defined by human perception. Two alternative algorithms are presented, one which performs Rao-Blackwellisation to produce an almost deterministic formulation while the second is a formulation which models tempo as a Brownian motion process. The algorithms have been tested on a large and varied database of examples and results are comparable with the current state of the art. The deterministic algorithm gives the better performance of the two algorithms.

Optimization-based particle filter for state and parameter estimation

Institute of Scientific and Technical Information of China (English)

Li Fu; Qi Fei; Shi Guangming; Zhang Li

2009-01-01

In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Directory of Open Access Journals (Sweden)

Mohammadreza Asghari Oskoei

2017-11-01

Full Text Available This paper proposes an adaptive Kalman filter (AKF to improve the performance of a vision-based human machine interface (HMI applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.

Ultrasound effects on the electrolytically controlled etching of nuclear track filters (NTFs)

International Nuclear Information System (INIS)

Chakarvarti, S.K.; Mahna, S.K.; Sud, L.V.; Singh, P.

1990-01-01

The mechanical stirring of the etchant creates tremendous changes in the etching properties of SSNTDs. Ultrasound stirring also produces a number of effects in liquids by giving a rapid movement to etchant. Cavitation is the most probable phenomenon caused by ultrasound and responsible for most of the effects observed in chemical reactions. Microbubbles are created in liquid medium and explosion of these microbubbles is responsible for momentarily rise in temperature. The possible effects of ultrasound on etching of particle tracks in plastic track detectors as nuclear track filters has been studied. The ultrasound effects on V t and V b have been studied in this work. (author). 5 re fs

Approximate effect of parameter pseudonoise intensity on rate of convergence for EKF parameter estimators. [Extended Kalman Filter

Science.gov (United States)

Hill, Bryon K.; Walker, Bruce K.

1991-01-01

When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.

Auto-Calibration Methods of Kinematic Parameters and Magnetometer Offset for the Localization of a Tracked Mobile Robot

OpenAIRE

Luciano Cantelli; Samuel Ligama; Giovanni Muscato; Davide Spina

2016-01-01

This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demi...

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

Science.gov (United States)

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

Directory of Open Access Journals (Sweden)

Jian Ma

Full Text Available The aircraft environmental control system (ECS is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

CATS: a cellular automaton for tracking in silicon for the HERA-B vertex detector

International Nuclear Information System (INIS)

Abt, I.; Emeliyanov, D.; Kisel, I.; Masciocchi, S.

2002-01-01

The new track reconstruction program CATS developed for the Vertex Detector System of the HERA-B experiment at DESY is presented. It employs a cellular automaton for track searching and the Kalman filter for track fitting. This results in a very fast algorithm that combines highly efficient track recognition with accurate and reliable track parameter estimation. To reduce the computational cost of the fit an optimized numerical implementation of the Kalman filter is used. Alternative approaches to the track reconstruction in the VDS are also discussed. Since 1999, after extensive tests on simulated data, CATS has been employed to reconstruct experimental data collected in HERA-B. Results regarding tracking performance, the accuracy of track parameter estimates and CPU time consumption are presented

Parameter tracking with partial forgetting method

Czech Academy of Sciences Publication Activity Database

Dedecius, Kamil; Nagy, Ivan; Kárný, Miroslav

2012-01-01

Roč. 26, č. 1 (2012), s. 1-12 ISSN 0890-6327 R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : regression models * model * parameter estimation * parameter tracking Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.219, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-0370448.pdf

Guitarist Fingertip Tracking by Integrating a Bayesian Classifier into Particle Filters

Directory of Open Access Journals (Sweden)

Chutisant Kerdvibulvech

2008-01-01

Full Text Available We propose a vision-based method for tracking guitar fingerings made by guitar players. We present it as a new framework for tracking colored finger markers by integrating a Bayesian classifier into particle filters. This adds the useful abilities of automatic track initialization and recovery from tracking failures in a dynamic background. Furthermore, by using the online adaptation of color probabilities, this method is able to cope with illumination changes. Augmented Reality Tag (ARTag is then utilized to calculate the projection matrix as an online process which allows the guitar to be moved while being played. Representative experimental results are also included. The method presented can be used to develop the application of human-computer interaction (HCI to guitar playing by recognizing the chord being played by a guitarist in virtual spaces. The aforementioned application would assist guitar learners by allowing them to automatically identify if they are using the correct chords required by the musical piece.

Procedure manual for the estimation of average indoor radon-daughter concentrations using the filtered alpha-track method

International Nuclear Information System (INIS)

George, J.L.

1988-04-01

One of the measurement needs of US Department of Energy (DOE) remedial action programs is the estimation of the annual-average indoor radon-daughter concentration (RDC) in structures. The filtered alpha-track method, using a 1-year exposure period, can be used to accomplish RDC estimations for the DOE remedial action programs. This manual describes the procedure used to obtain filtered alpha-track measurements to derive average RDC estimates from the measurrements. Appropriate quality-assurance and quality-control programs are also presented. The ''prompt'' alpha-track method of exposing monitors for 2 to 6 months during specific periods of the year is also briefly discussed in this manual. However, the prompt alpha-track method has been validated only for use in the Mesa County, Colorado, area. 3 refs., 3 figs

Impact of Physician Education and a Dedicated Inferior Vena Cava Filter Tracking System on Inferior Vena Cava Filter Use and Retrieval Rates Across a Large US Health Care Region.

Science.gov (United States)

Wang, Stephen L; Cha, Hsien-Hwa A; Lin, James R; Francis, Bolanos; Elizabeth, Wakley; Martin, Porras; Rajan, Sudhir

2016-05-01

To evaluate the effects of physician familiarity with current evidence and guidelines on inferior vena cava (IVC) filter use and the availability of IVC filter tracking infrastructure on retrieval rates. Fourteen continuing medical education-approved in-hospital grand rounds covering evidence-based review of the literature on IVC filter efficacy, patient-centered outcomes, guidelines for IVC filter indications, and complications were performed across a large United States (US) health care region serving more than 3.5 million members. A computer-based IVC filter tracking system was deployed simultaneously. IVC filter use, rates of attempted retrieval, and fulfillment of guidelines for IVC filter indications were retrospectively evaluated at each facility for 12 months before intervention (n = 427) and for 12 months after intervention (n = 347). After education, IVC filter use decreased 18.7%, with a member enrollment-adjusted decrease of 22.2%, despite an increasing IVC filter use trend for 4 years. Reduction in IVC filter use at each facility strongly correlated with physician attendance at grand rounds (r = -0.69; P = .007). Rates of attempted retrieval increased from 38.9% to 54.0% (P = .0006), with similar rates of successful retrieval (82.3% before education and 85.8% after education on first attempt). Improvement in IVC filter retrieval attempts correlated with physician attendance at grand rounds (r = 0.51; P = .051). IVC filter dwell times at first retrieval attempt were similar (10.2 wk before and 10.8 wk after). Physician education dramatically reduced IVC filter use across a large US health care region, and represents a learning opportunity for physicians who request and place them. Education and a novel tracking system improved rates of retrieval for IVC filter devices. Copyright © 2016 SIR. Published by Elsevier Inc. All rights reserved.

Auto-Calibration Methods of Kinematic Parameters and Magnetometer Offset for the Localization of a Tracked Mobile Robot

Directory of Open Access Journals (Sweden)

Luciano Cantelli

2016-11-01

Full Text Available This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demining operations.

Arbitrary-step randomly delayed robust filter with application to boost phase tracking

Science.gov (United States)

Qin, Wutao; Wang, Xiaogang; Bai, Yuliang; Cui, Naigang

2018-04-01

The conventional filters such as extended Kalman filter, unscented Kalman filter and cubature Kalman filter assume that the measurement is available in real-time and the measurement noise is Gaussian white noise. But in practice, both two assumptions are invalid. To solve this problem, a novel algorithm is proposed by taking the following four steps. At first, the measurement model is modified by the Bernoulli random variables to describe the random delay. Then, the expression of predicted measurement and covariance are reformulated, which could get rid of the restriction that the maximum number of delay must be one or two and the assumption that probabilities of Bernoulli random variables taking the value one are equal. Next, the arbitrary-step randomly delayed high-degree cubature Kalman filter is derived based on the 5th-degree spherical-radial rule and the reformulated expressions. Finally, the arbitrary-step randomly delayed high-degree cubature Kalman filter is modified to the arbitrary-step randomly delayed high-degree cubature Huber-based filter based on the Huber technique, which is essentially an M-estimator. Therefore, the proposed filter is not only robust to the randomly delayed measurements, but robust to the glint noise. The application to the boost phase tracking example demonstrate the superiority of the proposed algorithms.

Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles

Directory of Open Access Journals (Sweden)

Yiqing Huang

2016-11-01

Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.

Track reconstruction at the ILC: the ILD tracking software

International Nuclear Information System (INIS)

Gaede, Frank; Aplin, Steven; Rosemann, Christoph; Voutsinas, Georgios; Glattauer, Robin

2014-01-01

One of the key requirements for Higgs physics at the International Linear Collider ILC is excellent track reconstruction with very good momentum and impact parameter resolution. ILD is one of the two detector concepts at the ILC. Its central tracking system comprises of an outer Si-tracker, a highly granular TPC, an intermediate silicon tracker and a pixel vertex detector, and it is complemented by silicon tracking disks in the forward direction. Large hit densities from beam induced coherent electron-positron pairs at the ILC pose an additional challenge to the pattern recognition algorithms. We present the recently developed new ILD tracking software, the pattern recognition algorithms that are using clustering techniques, Cellular Automatons and Kalman filter based track extrapolation. The performance of the ILD tracking system is evaluated using a detailed simulation including dead material, gaps and imperfections.

Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles

Science.gov (United States)

Li, Shengbo Eben; Li, Guofa; Yu, Jiaying; Liu, Chang; Cheng, Bo; Wang, Jianqiang; Li, Keqiang

2018-01-01

Detection and tracking of objects in the side-near-field has attracted much attention for the development of advanced driver assistance systems. This paper presents a cost-effective approach to track moving objects around vehicles using linearly arrayed ultrasonic sensors. To understand the detection characteristics of a single sensor, an empirical detection model was developed considering the shapes and surface materials of various detected objects. Eight sensors were arrayed linearly to expand the detection range for further application in traffic environment recognition. Two types of tracking algorithms, including an Extended Kalman filter (EKF) and an Unscented Kalman filter (UKF), for the sensor array were designed for dynamic object tracking. The ultrasonic sensor array was designed to have two types of fire sequences: mutual firing or serial firing. The effectiveness of the designed algorithms were verified in two typical driving scenarios: passing intersections with traffic sign poles or street lights, and overtaking another vehicle. Experimental results showed that both EKF and UKF had more precise tracking position and smaller RMSE (root mean square error) than a traditional triangular positioning method. The effectiveness also encourages the application of cost-effective ultrasonic sensors in the near-field environment perception in autonomous driving systems.

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ Tracking Filter

Directory of Open Access Journals (Sweden)

Kenshi Saho

2017-07-01

Full Text Available We present a strategy for designing an α - β - η - θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α - β filter. Moreover, we apply an α - β - η - θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α - β - η - θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target’s acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.

HCP track calculations in Lif:Mg,Ti: 3D modeling of the ''track – escape'' parameter

International Nuclear Information System (INIS)

Sattinger, D.; Sharon, A.; Horowitz, Y.S.

2011-01-01

The conceptual framework of the track interaction model (TIM) was conceived in the 1970s and mathematically formulated in the 1980s to describe heavy charged particle TL fluence response supralinearity. The extended track interaction model (ETIM) was developed to include saturation effects due to overlapping tracks and has been applied to both proton and alpha particle TL fluence response. One of the parameters of major importance in the TIM is the ''track – escape'' parameter, defined by N e /N w , where N e represents the number of electrons which escape the parent track during heating, and N w is the number of electrons which recombine within the parent track to produce a TL photon. Recently a first attempt was carried out to theoretically model escape parameters calculated in 2D geometry as a function of particle type and energy using trapping center (TC), luminescent center (LC) and competitive center (CC) occupation probabilities calculated from track segment radial dose distributions and optical absorption (OA) dose response. In this study, the calculations are extended to 3D geometry using a Monte Carlo approach which samples the point of creation of the charge carriers according to the TC occupation probabilities and then estimates N w by sampling the chord length to the track exterior. Charge carriers which escape the irradiated track volume contribute to N e . This more sophisticated 3D calculation of N e /N w is expected to increase the reliability of the modeling of heavy charged particle TL fluence response in the framework of the ETIM and enhance our understanding of “track effects” in Heavy Charged Particle (HCP) induced TL.

Rapid estimation of high-parameter auditory-filter shapes

Science.gov (United States)

Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M.

2014-01-01

A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086

Analysis of multidimensional difference-of-Gaussians filters in terms of directly observable parameters.

Science.gov (United States)

Cope, Davis; Blakeslee, Barbara; McCourt, Mark E

2013-05-01

The difference-of-Gaussians (DOG) filter is a widely used model for the receptive field of neurons in the retina and lateral geniculate nucleus (LGN) and is a potential model in general for responses modulated by an excitatory center with an inhibitory surrounding region. A DOG filter is defined by three standard parameters: the center and surround sigmas (which define the variance of the radially symmetric Gaussians) and the balance (which defines the linear combination of the two Gaussians). These parameters are not directly observable and are typically determined by nonlinear parameter estimation methods applied to the frequency response function. DOG filters show both low-pass (optimal response at zero frequency) and bandpass (optimal response at a nonzero frequency) behavior. This paper reformulates the DOG filter in terms of a directly observable parameter, the zero-crossing radius, and two new (but not directly observable) parameters. In the two-dimensional parameter space, the exact region corresponding to bandpass behavior is determined. A detailed description of the frequency response characteristics of the DOG filter is obtained. It is also found that the directly observable optimal frequency and optimal gain (the ratio of the response at optimal frequency to the response at zero frequency) provide an alternate coordinate system for the bandpass region. Altogether, the DOG filter and its three standard implicit parameters can be determined by three directly observable values. The two-dimensional bandpass region is a potential tool for the analysis of populations of DOG filters (for example, populations of neurons in the retina or LGN), because the clustering of points in this parameter space may indicate an underlying organizational principle. This paper concentrates on circular Gaussians, but the results generalize to multidimensional radially symmetric Gaussians and are given as an appendix.

Determining of the track parameters in solid state nuclear track detectors Cr 39 due to alpha particles

International Nuclear Information System (INIS)

Kostic, D.; Nikezic, D.

1997-01-01

An equation of the etch pit wall is proposed to be used for simulation of the track growth and calculating the major and the minor axis of etch pit opening. Dependence on the following parameters is set up: distance along a track from the point where the particle entered the detector, ratio of the track etch wall to the bulk etch rate, integration constant determined from particle penetration depth and normal distance from the particle trajectory to the etch pit wall. The corresponding computer program was written. The input parameters of this program are: alpha particles energy, incidence angle and removed layer; the output gives track parameters. The results obtained by this method are compared to another approach given by Somogy and Szalay (1973) and a reasonably good agreement is found. (author)

Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking

Directory of Open Access Journals (Sweden)

Anders M. Johansson

2007-01-01

Full Text Available In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD scheme. We describe a new ASLT algorithm based on a particle filtering (PF approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.

Particle tracking from image sequences of complex plasma crystals

International Nuclear Information System (INIS)

Hadziavdic, Vedad; Melandsoe, Frank; Hanssen, Alfred

2006-01-01

In order to gather information about the physics of the complex plasma crystals from the experimental data, particles have to be tracked through a sequence of images. An application of the Kalman filter for that purpose is presented, using a one-dimensional approximation of the particle dynamics as a model for the filter. It is shown that Kalman filter is capable of tracking dust particles even with high levels of measurement noise. An inherent part of the Kalman filter, the innovation process, can be used to estimate values of the physical system parameters from the experimental data. The method is shown to be able to estimate the characteristic oscillation frequency from noisy data

Track filtering by robust neural network

International Nuclear Information System (INIS)

Baginyan, S.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.

1993-01-01

In the present paper we study the following problems of track information extraction by the artificial neural network (ANN) rotor model: providing initial ANN configuration by an algorithm general enough to be applicable for any discrete detector in- or out of a magnetic field; robustness to heavy contaminated raw data (up to 100% signal-to-noise ratio); stability to the growing event multiplicity. These problems were carried out by corresponding innovations of our model, namely: by a special one-dimensional histogramming, by multiplying weights by a specially designed robust multiplier, and by replacing the simulated annealing schedule by ANN dynamics with an optimally fixed temperature. Our approach is valid for both circular and straight (non-magnetic) tracks and tested on 2D simulated data contaminated by 100% noise points distributed uniformly. To be closer to some reality in our simulation, we keep parameters of the cylindrical spectrometer ARES. 12 refs.; 9 figs

An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

DEFF Research Database (Denmark)

Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

2016-01-01

, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

Science.gov (United States)

Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

2017-05-01

Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains.

Science.gov (United States)

Szczęsna, Agnieszka; Pruszowski, Przemysław

2016-01-01

Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.

A motion-compensated image filter for low-dose fluoroscopy in a real-time tumor-tracking radiotherapy system

International Nuclear Information System (INIS)

Miyamoto, Naoki; Ishikawa, Masayori; Sutherland, Kenneth

2015-01-01

In the real-time tumor-tracking radiotherapy system, a surrogate fiducial marker inserted in or near the tumor is detected by fluoroscopy to realize respiratory-gated radiotherapy. The imaging dose caused by fluoroscopy should be minimized. In this work, an image processing technique is proposed for tracing a moving marker in low-dose imaging. The proposed tracking technique is a combination of a motion-compensated recursive filter and template pattern matching. The proposed image filter can reduce motion artifacts resulting from the recursive process based on the determination of the region of interest for the next frame according to the current marker position in the fluoroscopic images. The effectiveness of the proposed technique and the expected clinical benefit were examined by phantom experimental studies with actual tumor trajectories generated from clinical patient data. It was demonstrated that the marker motion could be traced in low-dose imaging by applying the proposed algorithm with acceptable registration error and high pattern recognition score in all trajectories, although some trajectories were not able to be tracked with the conventional spatial filters or without image filters. The positional accuracy is expected to be kept within ±2 mm. The total computation time required to determine the marker position is a few milliseconds. The proposed image processing technique is applicable for imaging dose reduction. (author)

Nonlinear Vibration Signal Tracking of Large Offshore Bridge Stayed Cable Based on Particle Filter

Directory of Open Access Journals (Sweden)

Ye Qingwei

2015-12-01

Full Text Available The stayed cables are key stress components of large offshore bridge. The fault detection of stayed cable is very important for safe of large offshore bridge. A particle filter model and algorithm of nonlinear vibration signal are used in this paper. Firstly, the particle filter model of stayed cable of large offshore bridge is created. Nonlinear dynamic model of the stayed-cable and beam coupling system is dispersed in temporal dimension by using the finite difference method. The discrete nonlinear vibration equations of any cable element are worked out. Secondly, a state equation of particle filter is fitted by least square algorithm from the discrete nonlinear vibration equations. So the particle filter algorithm can use the accurate state equations. Finally, the particle filter algorithm is used to filter the vibration signal of bridge stayed cable. According to the particle filter, the de-noised vibration signal can be tracked and be predicted for a short time accurately. Many experiments are done at some actual bridges. The simulation experiments and the actual experiments on the bridge stayed cables are all indicating that the particle filter algorithm in this paper has good performance and works stably.

Tracking performance of unbalanced QPSK demodulators. I - Biphase Costas loop with passive arm filters

Science.gov (United States)

Simon, M. K.; Alem, W. K.

1978-01-01

Unbalanced quadriphase-shift-keying (QPSK) is an attractive means for transmitting two digital data streams which in general have different average powers, data rates, and data formats. Previous analyses of the tracking performance of Costas loop demodulators of unbalanced QPSK have accounted only for the filtering effect produced by the loop's two arm filters on the equivalent additive noise perturbing the loop. When the bandwidth of these filters is selected on the basis of the order of the data rate, as is typical of optimum Costas loop design, the filtering degradations of the data modulations themselves and the cross-modulation noise produced by their multiplication in the loop often cannot be neglected. The purpose of this paper is to incorporate these additional filtering effects into the analysis. Many of the results obtained herein are in the form of closed-form expressions which can easily be evaluated numerically for design and performance prediction purposes.

Track-before-detect procedures for detection of extended object

Science.gov (United States)

Fan, Ling; Zhang, Xiaoling; Shi, Jun

2011-12-01

In this article, we present a particle filter (PF)-based track-before-detect (PF TBD) procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance). Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.

Kalman filter based data fusion for neutral axis tracking in wind turbine towers

DEFF Research Database (Denmark)

Soman, Rohan; Malinowski, Pawel; Ostachowicz, Wieslaw

2015-01-01

downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even...... in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine....

A comparative study of track reconstruction methods in the context of CMS physics

International Nuclear Information System (INIS)

Winkler, M.

2002-05-01

This thesis deals mainly with the problem of reconstructing the tracks of charged particles from the measurements recorded by the CMS Tracker, in particular with track fitting. The task of the track fit is to use the available information (the subsets of hits belonging to the track of a particle found by a track finder) in a statistically optimal way for the estimation of the track parameters and the associated errors. Due to the high track multiplicity at LHC and the large background of noise hits, combinatorial methods such as the Kalman filter may have problems in the association of hits to tracks, and their results will be biased in the case of wrong hits assigned to a track. Recently developped non-linear, adaptive methods such as the deterministic annealing filter and the multi track filter try to solve the assignment problem during the track fit, by determining an assignment probability of a hit to a track. The methods are studied in the CMS Tracker using various event topologies. (author)

Dual-Channel Particle Filter Based Track-Before-Detect for Monopulse Radar

Directory of Open Access Journals (Sweden)

Fei Cai

2014-01-01

Full Text Available A particle filter based track-before-detect (PF-TBD algorithm is proposed for the monopulse high pulse repetition frequency (PRF pulse Doppler radar. The actual measurement model is adopted, in which the range is highly ambiguous and the sum and difference channels exist in parallel. A quantization method is used to approximate the point spread function to reduce the computation load. The detection decisions of the PF-TBD are fed to a binary integrator to further improve the detection performance. Simulation results show that the proposed algorithm can detect and track the low SNR target efficiently. The detection performance is improved significantly for both the single frame and the multiframe detection compared with the classical detector. A performance comparison with the PF-TBD using sum channel only is also supplied.

Adaptive filtering for hidden node detection and tracking in networks.

Science.gov (United States)

Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

2017-07-01

The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

Improved Particle Filter for Passive Target Tracking%改进粒子滤波在被动目标跟踪中的应用

Institute of Scientific and Technical Information of China (English)

邓小龙; 谢剑英; 杨煜普

2005-01-01

As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter (EKF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others.

Computation of nuclear reactor parameters using a stretch Kalman filtering

International Nuclear Information System (INIS)

Zwingelstein, G.; Poujol, A.

1976-01-01

A method of nonlinear stochastic filtering, the stretched Karman filter, is used for the estimation of two basic parameters involved in the control of nuclear reactor start-up. The corresponding algorithm is stored in a small Multi-8 computer and tested with data recorded for the Ulysse reactor (I.N.S.T.N.). The various practical problems involved in using the algorithm are examined: filtering initialization, influence of the model... The quality and time saving obtained in the computation make it possible for a real time operation, the computer being connected with the reactor [fr

Track-before-detect procedures for detection of extended object

Directory of Open Access Journals (Sweden)

Fan Ling

2011-01-01

Full Text Available Abstract In this article, we present a particle filter (PF-based track-before-detect (PF TBD procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance. Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.

A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter

Directory of Open Access Journals (Sweden)

Bizhong Xia

2015-11-01

Full Text Available The estimation of state of charge (SOC is a crucial evaluation index in a battery management system (BMS. The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST and New European Driving Cycle (NEDC. Making a comparison with extended Kalman filter (EKF and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness.

Application of Bayesian Maximum Entropy Filter in parameter calibration of groundwater flow model in PingTung Plain

Science.gov (United States)

Cheung, Shao-Yong; Lee, Chieh-Han; Yu, Hwa-Lung

2017-04-01

Due to the limited hydrogeological observation data and high levels of uncertainty within, parameter estimation of the groundwater model has been an important issue. There are many methods of parameter estimation, for example, Kalman filter provides a real-time calibration of parameters through measurement of groundwater monitoring wells, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applied in groundwater research. However, Kalman Filter method is limited to linearity. This study propose a novel method, Bayesian Maximum Entropy Filtering, which provides a method that can considers the uncertainty of data in parameter estimation. With this two methods, we can estimate parameter by given hard data (certain) and soft data (uncertain) in the same time. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation.

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Directory of Open Access Journals (Sweden)

Hongtao Yang

2018-01-01

Full Text Available This paper proposes a novel strong tracking filter (STF, which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the one-step random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP. Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.

Robust and Adaptive Block Tracking Method Based on Particle Filter

Directory of Open Access Journals (Sweden)

Bin Sun

2015-10-01

Full Text Available In the field of video analysis and processing, object tracking is attracting more and more attention especially in traffic management, digital surveillance and so on. However problems such as objects’ abrupt motion, occlusion and complex target structures would bring difficulties to academic study and engineering application. In this paper, a fragmentsbased tracking method using the block relationship coefficient is proposed. In this method, we use particle filter algorithm and object region is divided into blocks initially. The contribution of this method is that object features are not extracted just from a single block, the relationship between current block and its neighbor blocks are extracted to describe the variation of the block. Each block is weighted according to the block relationship coefficient when the block is voted on the most matched region in next frame. This method can make full use of the relationship between blocks. The experimental results demonstrate that our method can provide good performance in condition of occlusion and abrupt posture variation.

H(infinity)/H(2)/Kalman filtering of linear dynamical systems via variational techniques with applications to target tracking

Science.gov (United States)

Rawicz, Paul Lawrence

In this thesis, the similarities between the structure of the H infinity, H2, and Kalman filters are examined. The filters used in this examination have been derived through duality to the full information controller. In addition, a direct variation of parameters derivation of the Hinfinity filter is presented for both continuous and discrete time (staler case). Direct and controller dual derivations using differential games exist in the literature and also employ variational techniques. Using a variational, rather than a differential games, viewpoint has resulted in a simple relationship between the Riccati equations that arise from the derivation and the results of the Bounded Real Lemma. This same relation has previously been found in the literature and used to relate the Riccati inequality for linear systems to the Hamilton Jacobi inequality for nonlinear systems when implementing the Hinfinity controller. The Hinfinity, H2, and Kalman filters are applied to the two-state target tracking problem. In continuous time, closed form analytic expressions for the trackers and their performance are determined. To evaluate the trackers using a neutral, realistic, criterion, the probability of target escape is developed. That is, the probability that the target position error will be such that the target is outside the radar beam width resulting in a loss of measurement. In discrete time, a numerical example, using the probability of target escape, is presented to illustrate the differences in tracker performance.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

Science.gov (United States)

Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

International Nuclear Information System (INIS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-01-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters. (paper)

Determination of nuclear tracks parameters on sequentially etched PADC detectors

Science.gov (United States)

Horwacik, Tomasz; Bilski, Pawel; Koerner, Christine; Facius, Rainer; Berger, Thomas; Nowak, Tomasz; Reitz, Guenther; Olko, Pawel

Polyallyl Diglycol Carbonate (PADC) detectors find many applications in radiation protection. One of them is the cosmic radiation dosimetry, where PADC detectors measure the linear energy transfer (LET) spectra of charged particles (from protons to heavy ions), supplementing TLD detectors in the role of passive dosemeter. Calibration exposures to ions of known LET are required to establish a relation between parameters of track observed on the detector and LET of particle creating this track. PADC TASTRAK nuclear track detectors were exposed to 12 C and 56 Fe ions of LET in H2 O between 10 and 544 keV/µm. The exposures took place at the Heavy Ion Medical Accelerator (HIMAC) in Chiba, Japan in the frame of the HIMAC research project "Space Radiation Dosimetry-Ground Based Verification of the MATROSHKA Facility" (20P-240). Detectors were etched in water solution of NaOH with three different temperatures and for various etching times to observe the appearance of etched tracks, the evolution of their parameters and the stability of the etching process. The applied etching times (and the solution's concentrations and temperatures) were: 48, 72, 96, 120 hours (6.25 N NaOH, 50 O C), 20, 40, 60, 80 hours (6.25 N NaOH, 60 O C) and 8, 12, 16, 20 hours (7N NaOH, 70 O C). The analysis of the detectors involved planimetric (2D) measurements of tracks' entrance ellipses and mechanical measurements of bulk layer thickness. Further track parameters, like angle of incidence, track length and etch rate ratio were then calculated. For certain tracks, results of planimetric measurements and calculations were also compared with results of optical track profile (3D) measurements, where not only the track's entrance ellipse but also the location of the track's tip could be directly measured. All these measurements have been performed with the 2D/3D measurement system at DLR. The collected data allow to create sets of V(LET in H2 O) calibration curves suitable for short, intermediate and

Effect of cross-correlation on track-to-track fusion

Science.gov (United States)

Saha, Rajat K.

1994-07-01

Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which performs track-to-track association as well as kinematic state vector fusion. This paper seeks to answer analytically the extent of improvement achievable by means of kinetic state vector fusion when the tracks are obtained from dissimilar sensors (e.g., Radar/ESM/IRST/IFF). It is well known that evaluation of the performance of state vector fusion algorithms at steady state must take into account the effects of cross-correlation between eligible tracks introduced by the input noise which, unfortunately, is often neglected because of added computational complexity. In this paper, an expression for the steady-state cross-covariance matrix for a 2D state vector track-to-track fusion is obtained. This matrix is shown to be a function of the parameters of the Kalman filters associated with the candidate tracks being fused. Conditions for positive definiteness of the cross-covariance matrix have been derived and the effect of positive definiteness on performance of track-to-track fusion is also discussed.

State and parameter estimation of the heat shock response system using Kalman and particle filters.

Science.gov (United States)

Liu, Xin; Niranjan, Mahesan

2012-06-01

Traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this article, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system. Here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (i) extended Kalman filter (EKF), (ii) unscented Kalman filter (UKF) and (iii) the particle filter, in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium Escherichia coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values. Software implementation of the three filters on this problem can be freely downloaded from http://users.ecs.soton.ac.uk/mn/HeatShock

Analysis of the selected mechanical parameters of coating of filters protecting against hazardous infrared radiation.

Science.gov (United States)

Gralewicz, Grzegorz; Owczarek, Grzegorz; Kubrak, Janusz

2017-03-01

This article presents a comparison of the test results of selected mechanical parameters (hardness, Young's modulus, critical force for delamination) for protective filters intended for eye protection against harmful infrared radiation. Filters with reflective metallic films were studied, as well as interference filters developed at the Central Institute for Labour Protection - National Research Institute (CIOP-PIB). The test results of the selected mechanical parameters were compared with the test results, conducted in accordance with a standardised method, of simulating filter surface destruction that occurs during use.

Radar tracking with an interacting multiple model and probabilistic data association filter for civil aviation applications.

Science.gov (United States)

Jan, Shau-Shiun; Kao, Yu-Chun

2013-05-17

The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC) system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM) system that is enabled by global positioning system (GPS) technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF), is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF), is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.

Radar Tracking with an Interacting Multiple Model and Probabilistic Data Association Filter for Civil Aviation Applications

Directory of Open Access Journals (Sweden)

Shau-Shiun Jan

2013-05-01

Full Text Available The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM system that is enabled by global positioning system (GPS technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF, is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF, is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.

Track reconstruction principle in ALICE for LHC run I and run II

CERN Multimedia

Maire, Antonin

2011-01-01

Principles of tracking for an ALICE event, showing the three successive paths allowing to build a track and refine its parameters. Numbers ranging from 1 to 10 mention the bits that are activated in case of success during the propgation of the Kalman filter at the considered stage.

Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

Science.gov (United States)

Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

2017-01-01

A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.

A Novel Generic Framework for Track Fitting in Complex Detector Systems

OpenAIRE

Höppner, C.; Neubert, S.; Ketzer, B.; Paul, S.

2009-01-01

This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices th...

Etched track radiometers in radon measurements: a review

CERN Document Server

Nikolaev, V A

1999-01-01

Passive radon radiometers, based on alpha particle etched track detectors, are very attractive for the assessment of radon exposure. The present review considers various devices used for measurement of the volume activity of radon isotopes and their daughters and determination of equilibrium coefficients. Such devices can be classified into 8 groups: (i) open or 'bare' detectors, (ii) open chambers, (iii) sup 2 sup 2 sup 2 Rn chambers with an inlet filter, (iv) advanced sup 2 sup 2 sup 2 Rn radiometers, (v) multipurpose radiometers, (vi) radiometers based on a combination of etched track detectors and an electrostatic field, (vii) radiometers based on etched track detectors and activated charcoal and (viii) devices for the measurement of radon isotopes and/or radon daughters by means of track parameter measurements. Some of them such as the open detector and the chamber with an inlet filter have a variety of modifications and are applied widely both in geophysical research and radon dosimetric surveys. At the...

Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

Science.gov (United States)

Chen, Xiyuan; Wang, Xiying; Xu, Yuan

2014-12-09

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

Sky-Hook Control and Kalman Filtering in Nonlinear Model of Tracked Vehicle Suspension System

Directory of Open Access Journals (Sweden)

Jurkiewicz Andrzej

2017-09-01

Full Text Available The essence of the undertaken topic is application of the continuous sky-hook control strategy and the Extended Kalman Filter as the state observer in the 2S1 tracked vehicle suspension system. The half-car model of this suspension system consists of seven logarithmic spiral springs and two magnetorheological dampers which has been described by the Bingham model. The applied continuous sky-hook control strategy considers nonlinear stiffness characteristic of the logarithmic spiral springs. The control is determined on estimates generated by the Extended Kalman Filter. Improve of ride comfort is verified by comparing simulation results, under the same driving conditions, of controlled and passive vehicle suspension systems.

NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP

Institute of Scientific and Technical Information of China (English)

ZHOU Bo; HAN Jianda

2007-01-01

In order to achieve precise, robust autonomous guidance and control of a tracked vehicle, a kinematic model with longitudinal and lateral slip is established. Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly. The first filter is the well-known extended Kalman filter. The second filter is an unscented version of the Kalman filter. The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution. The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies. The four different approaches have different complexities, behavior and advantages that are surveyed and compared.

Track reconstruction algorithms for the CBM experiment at FAIR

International Nuclear Information System (INIS)

Lebedev, Andrey; Hoehne, Claudia; Kisel, Ivan; Ososkov, Gennady

2010-01-01

The Compressed Baryonic Matter (CBM) experiment at the future FAIR accelerator complex at Darmstadt is being designed for a comprehensive measurement of hadron and lepton production in heavy-ion collisions from 8-45 AGeV beam energy, producing events with large track multiplicity and high hit density. The setup consists of several detectors including as tracking detectors the silicon tracking system (STS), the muon detector (MUCH) or alternatively a set of Transition Radiation Detectors (TRD). In this contribution, the status of the track reconstruction software including track finding, fitting and propagation is presented for the MUCH and TRD detectors. The track propagation algorithm takes into account an inhomogeneous magnetic field and includes accurate calculation of multiple scattering and energy losses in the detector material. Track parameters and covariance matrices are estimated using the Kalman filter method and a Kalman filter modification by assigning weights to hits and using simulated annealing. Three different track finder algorithms based on track following have been developed which either allow for track branches, just select nearest hits or use the mentioned weighting method. The track reconstruction efficiency for central Au+Au collisions at 25 AGeV beam energy using events from the UrQMD model is at the level of 93-95% for both detectors.

Analysis of design parameters for crosstalk cancellation filters applied to different loudspeaker configurations

DEFF Research Database (Denmark)

Parodi, Yesenia Lacouture

2008-01-01

Several approaches to render binaural signals through loudspeakers have been proposed previously. Some studies had focused on the optimum loudspeaker arrangement while others had proposed efficient filters. However, to our knowledge, the identification of optimal parameters for inverse methods ap...... loudspeaker arrangements. Least square approximations in frequency and time domain are evaluated along with a crosstalk canceler based on minimum-phase approximation. Filter parameters, such as length and regularization, are varied and simulated for different span and elevation angles....

An Automatic Parameter Identification Method for a PMSM Drive with LC-Filter

DEFF Research Database (Denmark)

Bech, Michael Møller; Christensen, Jeppe Haals; Weber, Magnus L.

2016-01-01

of the PMSM fed through an LC-filter. Based on the measured current response, model parameters for both the filter (L, R, C) and the PMSM (L and R) are estimated: First, the frequency response of the system is estimated using Welch Modified Periodogram method and then an optimization algorithm is used to find...... the parameters in an analytical reference model that minimize the model error. To demonstrate the practical feasibility of the method, a fully functional drive including an embedded real-time controller has been built. In addition to modulation, data acquisition and control the whole parameter identification...... method is also implemented on the real-time controller. Based on laboratory experiments on a 22 kW drive, it is concluded that the embedded identification method can estimate the five parameters in less than ten seconds....

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration.

Science.gov (United States)

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-05-02

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.

Leader-Follower Tracking System for Agricultural Vehicles: Fusion of Laser and Odometry Positioning Using Extended Kalman Filter

Directory of Open Access Journals (Sweden)

Zhang Lin Huan

2015-03-01

Full Text Available The aim of this research was to develop a safe human-driven and autonomous leader-follower tracking system for an autonomous tractor. To enable the tracking system, a laser range finder (LRF-based landmark detection system was designed to observe the relative position between a leader and a follower used in agricultural operations. The virtual follower-based formation-tracking algorithm was developed to minimize tracking errors and ensure safety. An extended Kalman filter (EKF was implemented for fusing LRF and odometry position to ensure stability of tracking in noisy farmland conditions. Simulations were conducted for tracking the leader in small and large sinusoidal curved paths. Simulated results verified high accuracy of formation tracking, stable velocity, and regulated steering angle of the follower. The tracking method confirmed the follower could follow the leader with a required formation safely and steadily in noisy conditions. The EKF helped to improve observation accuracy, velocity, and steering angle stability of the follower. As a result of the improved accuracy of observation and motion action, the tracking performance for lateral, longitudinal, and heading were also improved after the EKF was implemented in the tracking system.

Analysis of the selected optical parameters of filters protecting against hazardous infrared radiation

OpenAIRE

Gralewicz, Grzegorz; Owczarek, Grzegorz

2016-01-01

The paper analyses the selected optical parameters of protective optic filters used for protection of the eyes against hazardous radiation within the visible (VIS) and near infrared (NIR) spectrum range. The indexes characterizing transmission and reflection of optic radiation incident on the filter are compared. As it follows from the completed analysis, the newly developed interference filters provide more effective blocking of infrared radiation in comparison with the currently used protec...

Kalman滤波在井下人员跟踪定位中的应用%Application of Kalman filter in underground personnel tracking and positioning

Institute of Scientific and Technical Information of China (English)

罗宇锋; 刘勇; 李芳

2013-01-01

In view of problem that RSSI location algorithm does not have continuity in positioning process,the paper proposed an underground personnel positioning method with continuity based on Kalman filter.The method adopts Kalman filter to filter processing position coordinates of underground personnel estimated by RSSI location algorithm,and established a Kalman filter model on the basis of this coordinate in order to real-timely track underground personnel.The experimental results show that the positioning method based on Kalman filter has accurate tracking effect,and can improve real-time performance and tracking accuracy of underground personnel tracking and positioning system.%针对基于RSSI定位算法在定位过程中不具备连续性问题,提出了一种基于Kalman滤波的连续性并下人员定位方法.采用Kalman滤波对基于RSSI定位算法估算出的井下人员位置坐标进行滤波处理,在此坐标的基础上,建立Kalman滤波模型,利用Kalman滤波实现对井下人员的实时跟踪.实验结果表明,基于Kalman滤波的定位方法对井下人员的跟踪效果较好,提高了系统的实时性和跟踪精度.

Predicting inferior vena cava (IVC) filter retrievability using positional parameters: A comparative study of various filter types.

Science.gov (United States)

Gotra, A; Doucet, C; Delli Fraine, P; Bessissow, A; Dey, C; Gallix, B; Boucher, L-M; Valenti, D

2018-05-14

To compare changes in inferior vena cava (IVC) filter positional parameters from insertion to removal and examine how they affect retrievability amongst various filter types. A total of 447 patients (260 men, 187 women) with a mean age of 55 years (range: 13-91 years) who underwent IVC filter retrieval between 2007-2014 were retrospectively included. Post-insertion and pre-retrieval angiographic studies were assessed for filter tilt, migration, strut wall penetration and retrieval outcomes. ANCOVA and multiple logistic regression models were used to analyze factors affecting retrieval success. Pairwise comparisons between filter types were performed. Of 488 IVC filter retrieval attempts, 94.1% were ultimately successful. The ALN filter had the highest mean absolute value of tilt (5.6 degrees), the Optease filter demonstrated the largest mean migration (-8.0mm) and the Bard G2 filter showed highest mean penetration (5.2mm). Dwell time of 0-90 days (OR, 11.1; P=0.01) or 90-180 days (OR, 2.6; P=0.02), net tilt of 10-15 degrees (OR 8.9; P=0.05), caudal migration of -10 to 0mm (OR, 3.46; P=0.03) and penetration less than 3mm (OR, 2.6; P=0.01) were positive predictors of successful retrievability. Higher odds of successful retrieval were obtained for the Bard G2X, Bard G2 and Cook Celect when compared to the ALN and Cordis Optease filters. Shorter dwell time, lower mean tilt, caudal migration and less caval wall penetration are positive predictors of successful IVC filter retrieval. Copyright © 2018 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Homogenous polynomially parameter-dependent H∞ filter designs of discrete-time fuzzy systems.

Science.gov (United States)

Zhang, Huaguang; Xie, Xiangpeng; Tong, Shaocheng

2011-10-01

This paper proposes a novel H(∞) filtering technique for a class of discrete-time fuzzy systems. First, a novel kind of fuzzy H(∞) filter, which is homogenous polynomially parameter dependent on membership functions with an arbitrary degree, is developed to guarantee the asymptotic stability and a prescribed H(∞) performance of the filtering error system. Second, relaxed conditions for H(∞) performance analysis are proposed by using a new fuzzy Lyapunov function and the Finsler lemma with homogenous polynomial matrix Lagrange multipliers. Then, based on a new kind of slack variable technique, relaxed linear matrix inequality-based H(∞) filtering conditions are proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

El Gharamti, Mohamad; Hoteit, Ibrahim

2014-01-01

The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.

Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

KAUST Repository

El Gharamti, Mohamad

2014-02-01

The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.

Frequency-scanning interferometry using a time-varying Kalman filter for dynamic tracking measurements.

Science.gov (United States)

Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen

2017-10-16

Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

Science.gov (United States)

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

Science.gov (United States)

Broquetas, Antoni; Comerón, Adolf; Gelonch, Antoni; Fuertes, Josep M.; Castro, J. Antonio; Felip, Damià; López, Miguel A.; Pulido, José A.

2012-01-01

The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns. PMID:23443376

Physics-based coastal current tomographic tracking using a Kalman filter.

Science.gov (United States)

Wang, Tongchen; Zhang, Ying; Yang, T C; Chen, Huifang; Xu, Wen

2018-05-01

Ocean acoustic tomography can be used based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area to invert/map the ocean current inside the area. Data at different times can be related using a Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different times are treated independently. Real data are also examined.

Development of an optimal automatic control law and filter algorithm for steep glideslope capture and glideslope tracking

Science.gov (United States)

Halyo, N.

1976-01-01

A digital automatic control law to capture a steep glideslope and track the glideslope to a specified altitude is developed for the longitudinal/vertical dynamics of a CTOL aircraft using modern estimation and control techniques. The control law uses a constant gain Kalman filter to process guidance information from the microwave landing system, and acceleration from body mounted accelerometer data. The filter outputs navigation data and wind velocity estimates which are used in controlling the aircraft. Results from a digital simulation of the aircraft dynamics and the control law are presented for various wind conditions.

An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

Science.gov (United States)

Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

2015-05-13

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

Directory of Open Access Journals (Sweden)

Yibo Feng

2015-05-01

Full Text Available We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF, the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

Heavy particle track structure parameters for biophysical modelling

International Nuclear Information System (INIS)

Watt, D.E.

1994-01-01

Averaged values of physical track structure parameters are important in radiobiology and radiological protection for the expression of damage mechanisms and for quantifying radiation effects. To provide a ready reference, tables of relevant quantities have been compiled for heavy charged particles in liquid water. The full tables will be published elsewhere but here illustrative examples are given of the trends for the most important quantities. In the tables, data are given for 74 types of heavy charged particle ranging from protons to uranium ions at specific energies between 0.1 keV/u and 1 GeV/u. Aggregate effects in liquid water are taken into account implicitly in the calculations. Results are presented for instantaneous particle energies and for averages over the charged particle equilibrium spectrum. The latter are of special relevance to radiation dosimetry. Quality parameters calculated are: β 2 ; z 2 /β 2 ; linear primary ionisation and the mean free path between ionisations; LET; track and dose-restricted LET with 100 eV cut-off; relative variances; delta-ray energies and ranges; ion energies and ranges and kerma factors. Here, the procedures used in the calculations are indicated. Representative results are shown in graphical form. The role of the physical track properties is discussed with regard to optimisation of the design of experiments intended to elucidate biological damage mechanisms in mammalian cells and their relevance to radiological protection. ((orig.))

Random Scenario Generation for a Multiple Target Tracking Environment Evaluation

DEFF Research Database (Denmark)

Hussain, Dil Muhammad Akbar

2006-01-01

, which were normally crossing targets, was to test the efficiency of the track splitting algorithm for different situations. However this approach only gives a measure of performance for a specific, possibly unrealistic, scenario and it was felt appropriate to develop procedures that would enable a more...... general performance assessment. Therefore, a random target motion scenario is adopted. Its implementation in particular for testing the track splitting algorithm using Kalman filters is used and a couple of tracking performance parameters are computed to investigate such random scenarios....

A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR

Institute of Scientific and Technical Information of China (English)

CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua

2005-01-01

Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.

A Modified Multifrequency Passivity-Based Control for Shunt Active Power Filter With Model-Parameter-Adaptive Capability

DEFF Research Database (Denmark)

Mu, Xiaobin; Wang, Jiuhe; Wu, Weimin

2018-01-01

The passivity-based control (PBC) has a better control performance using an accurate mathematical model of the control object. It can offer an alternative tracking control scheme for the shunt active power filter (SAPF). However, the conventional PBC-based SAPF cannot achieve zero steady...

Determination of boron in aqueous solutions by solid state nuclear track detectors technique, using a filtered neutron beam

International Nuclear Information System (INIS)

Moraes, M.A.P.V. de; Pugliesi, R.; Khouri, M.T.F.C.

1985-11-01

The solid state nuclear track detectors technique has been used for determination of boron in aqueous solutions, using a filtered neutron beam. The particles tracks from the 10 B(n,α)Li 7 reaction were registered in the CR-39 film, chemically etched in a (30%) KOH solution 70 0 C during 90 minutes. The obtained results showed the usefulness of this technique for boron determination in the ppm range. The inferior detectable limit was 9 ppm. The combined track registration efficiency factor K has been evaluated in the solutions, for the CR-39 detector and its values is K= (4,60 - + 0,06). 10 -4 cm. (Author) [pt

Full-field particle velocimetry with a photorefractive optical novelty filter

International Nuclear Information System (INIS)

Woerdemann, Mike; Holtmann, Frank; Denz, Cornelia

2008-01-01

We utilize the finite time constant of a photorefractive optical novelty filter microscope to access full-field velocity information of fluid flows on microscopic scales. In contrast to conventional methods such as particle image velocimetry and particle tracking velocimetry, not only image acquisition of the tracer particle field but also evaluation of tracer particle velocities is done all-optically by the novelty filter. We investigate the velocity dependent parameters of two-beam coupling based optical novelty filters and demonstrate calibration and application of a photorefractive velocimetry system. Theoretical and practical limits to the range of accessible velocities are discussed

Integrating retention soil filters into urban hydrologic models - Relevant processes and important parameters

Science.gov (United States)

Bachmann-Machnik, Anna; Meyer, Daniel; Waldhoff, Axel; Fuchs, Stephan; Dittmer, Ulrich

2018-04-01

Retention Soil Filters (RSFs), a form of vertical flow constructed wetlands specifically designed for combined sewer overflow (CSO) treatment, have proven to be an effective tool to mitigate negative impacts of CSOs on receiving water bodies. Long-term hydrologic simulations are used to predict the emissions from urban drainage systems during planning of stormwater management measures. So far no universally accepted model for RSF simulation exists. When simulating hydraulics and water quality in RSFs, an appropriate level of detail must be chosen for reasonable balancing between model complexity and model handling, considering the model input's level of uncertainty. The most crucial parameters determining the resultant uncertainties of the integrated sewer system and filter bed model were identified by evaluating a virtual drainage system with a Retention Soil Filter for CSO treatment. To determine reasonable parameter ranges for RSF simulations, data of 207 events from six full-scale RSF plants in Germany were analyzed. Data evaluation shows that even though different plants with varying loading and operation modes were examined, a simple model is sufficient to assess relevant suspended solids (SS), chemical oxygen demand (COD) and NH4 emissions from RSFs. Two conceptual RSF models with different degrees of complexity were assessed. These models were developed based on evaluation of data from full scale RSF plants and column experiments. Incorporated model processes are ammonium adsorption in the filter layer and degradation during subsequent dry weather period, filtration of SS and particulate COD (XCOD) to a constant background concentration and removal of solute COD (SCOD) by a constant removal rate during filter passage as well as sedimentation of SS and XCOD in the filter overflow. XCOD, SS and ammonium loads as well as ammonium concentration peaks are discharged primarily via RSF overflow not passing through the filter bed. Uncertainties of the integrated

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim

2016-01-01

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa

2016-08-12

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

Exploration and extension of an improved Riemann track fitting algorithm

Science.gov (United States)

Strandlie, A.; Frühwirth, R.

2017-09-01

Recently, a new Riemann track fit which operates on translated and scaled measurements has been proposed. This study shows that the new Riemann fit is virtually as precise as popular approaches such as the Kalman filter or an iterative non-linear track fitting procedure, and significantly more precise than other, non-iterative circular track fitting approaches over a large range of measurement uncertainties. The fit is then extended in two directions: first, the measurements are allowed to lie on plane sensors of arbitrary orientation; second, the full error propagation from the measurements to the estimated circle parameters is computed. The covariance matrix of the estimated track parameters can therefore be computed without recourse to asymptotic properties, and is consequently valid for any number of observation. It does, however, assume normally distributed measurement errors. The calculations are validated on a simulated track sample and show excellent agreement with the theoretical expectations.

State and parameter estimation in a nuclear fuel pin using the extended Kalman filter

International Nuclear Information System (INIS)

Feeley, J.J.

1979-03-01

The Kalman filter is a powerful tool for the design and analysis of stochastic systems. The general nature of the method permits such diverse applications as on-line state estimation in optimal control systems, as well as state and parameter estimation applications in data analysis and system identification. However, while there have been a large number of Kalman filter applications in the aerospace industry, there have been relatively few in the nuclear industry. The report describes some initial efforts made at the Idaho National Engineering Laboratory to gain experience with the methods of Kalman filtering and to test their applicability to nuclear engineering problems. Two specific cases were considered: first, a real-time state estimation problem using a hybrid computer where the process was simulated on the analog portion of the computer, and the Kalman filter was programmed on the digital portion; second, a system identification problem where a digital extended Kalman filter program was used to estimate states and parameters in a nuclear fuel pin using data generated both by actual experiments and computer simulations. The report contains a derivation of the Kalman filter equations, a development of the mathematical model of the nuclear fuel pin, a description of the computer programs used in the analysis, and a discussion of the results obtained

Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing

Directory of Open Access Journals (Sweden)

Yang Jun

2016-02-01

Full Text Available A multiple targets cognitive radar tracking method based on Compressed Sensing (CS is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramér-Rao Bounds (PCRB of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.

Assessment of the pseudo-tracking approach for the calculation of material acceleration and pressure fields from time-resolved PIV: part II. Spatio-temporal filtering

Science.gov (United States)

van Gent, P. L.; Schrijer, F. F. J.; van Oudheusden, B. W.

2018-04-01

The present study characterises the spatio-temporal filtering associated with pseudo-tracking. A combined theoretical and numerical assessment is performed that uses the relatively simple flow case of a two-dimensional Taylor vortex as analytical test case. An additional experimental assessment considers the more complex flow of a low-speed axisymmetric base flow, for which time-resolved tomographic PIV measurements and microphone measurements were obtained. The results of these assessments show how filtering along Lagrangian tracks leads to amplitude modulation of flow structures. A cut-off track length and spatial resolution are specified to support future applications of the pseudo-tracking approach. The experimental results show a fair agreement between PIV and microphone pressure data in terms of fluctuation levels and pressure frequency spectra. The coherence and correlation between microphone and PIV pressure measurements were found to be substantial and almost independent of the track length, indicating that the low-frequency behaviour of the flow could be reproduced regardless of the track length. It is suggested that a spectral analysis can be used inform the selection of a suitable track length and to estimate the local error margin of reconstructed pressure values.

Determination of Nuclear Track Parameters for LR-115 Detector by Using of MATLAB Software Technique

International Nuclear Information System (INIS)

AL-Jomaily, F.M.; AL-joburi, H.A.; Mheemeed, A.K.

2013-01-01

The nuclear track detector parameters, such as nuclear track diameter D(μm), number of track N T and area of track A T were determined by using MATLAB software technique for IR-115 detector irradiated by alpha particle from 241 Am source under 1.5, 2.5 and 3.5 MeV at etching time T B of 90, 120, 150 and 180 min.By using the image analysis of MATLAB software for nuclear track, the full width at half maximum FWHM and relative resolution R% were calculated for each energy of alpha particles.In this study, it was shown that increasing the alpha energy on the IR-115 detector leads to increased etching time T B and the dropping of R% to minimum value, and then reach a stable value before dropping at values 1.5, 2.5 MeV and unstable at 3.5 MeV. Imaging analysis by MATLAB technique which used in this study reflect good and accurate results for nuclear track detector parameters and we recommend using this technique for determination of these parameters

Human tracking in thermal images using adaptive particle filters with online random forest learning

Science.gov (United States)

Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

2013-11-01

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

Target Tracking of a Linear Time Invariant System under Irregular Sampling

Directory of Open Access Journals (Sweden)

Jin Xue-Bo

2012-11-01

Full Text Available Due to event-triggered sampling in a system, or maybe with the aim of reducing data storage, tracking many applications will encounter irregular sampling time. By calculating the matrix exponential using an inverse Laplace transform, this paper transforms the irregular sampling tracking problem to the problem of tracking with time-varying parameters of a system. Using the common Kalman filter, the developed method is used to track a target for the simulated trajectory and video tracking. The results of simulation experiments have shown that it can obtain good estimation performance even at a very high irregular rate of measurement sampling time.

A goal-oriented field measurement filtering technique for the identification of material model parameters

KAUST Repository

Lubineau, Gilles

2009-05-16

The post-processing of experiments with nonuniform fields is still a challenge: the information is often much richer, but its interpretation for identification purposes is not straightforward. However, this is a very promising field of development because it would pave the way for the robust identification of multiple material parameters using only a small number of experiments. This paper presents a goal-oriented filtering technique in which data are combined into new output fields which are strongly correlated with specific quantities of interest (the material parameters to be identified). Thus, this combination, which is nonuniform in space, constitutes a filter of the experimental outputs, whose relevance is quantified by a quality function based on global variance analysis. Then, this filter is optimized using genetic algorithms. © 2009 Springer-Verlag.

Calculation of track and vertex errors for detector design studies

International Nuclear Information System (INIS)

Harr, R.

1995-01-01

The Kalman Filter technique has come into wide use for charged track reconstruction in high-energy physics experiments. It is also well suited for detector design studies, allowing for the efficient estimation of optimal track covariance matrices without the need of a hit level Monte Carlo simulation. Although much has been published about the Kalman filter equations, there is a lack of previous literature explaining how to implement the equations. In this paper, the operators necessary to implement the Kalman filter equations for two common detector configurations are worked out: a central detector in a uniform solenoidal magnetic field, and a fixed-target detector with no magnetic field in the region of the interactions. With the track covariance matrices in hand, vertex and invariant mass errors are readily calculable. These quantities are particularly interesting for evaluating experiments designed to study weakly decaying particles which give rise to displaced vertices. The optimal vertex errors are obtained via a constrained vertex fit. Solutions are presented to the constrained vertex problem with and without kinematic constraints. Invariant mass errors are obtained via propagation of errors; the use of vertex constrained track parameters is discussed. Many of the derivations are new or previously unpublished

Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter

Science.gov (United States)

Simon, Ehouarn; Samuelsen, Annette; Bertino, Laurent; Mouysset, Sandrine

2015-12-01

A sequence of one-year combined state-parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical-biogeochemical model HYCOM-NORWECOM over the period 2007-2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom.

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.

Science.gov (United States)

Ligorio, Gabriele; Sabatini, Angelo M

2015-08-01

Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.

Robust estimation of track parameters in wire chambers

International Nuclear Information System (INIS)

Bogdanova, N.B.; Bourilkov, D.T.

1988-01-01

The aim of this paper is to compare numerically the possibilities of the least square fit (LSF) and robust methods for modelled and real track data to determine the linear regression parameters of charged particles in wire chambers. It is shown, that Tukey robust estimate is superior to more standard (versions of LSF) methods. The efficiency of the method is illustrated by tables and figures for some important physical characteristics

Application and Optimization of Kalman Filter for Baseband Signal Processing of GPS Receivers

Directory of Open Access Journals (Sweden)

He Yanpin

2016-01-01

Full Text Available High sensitivity tracking in GPS receiver is required in many weak signal circumstances. The key of improving sensitivity is the optimization of the loop filter in tracking. As Kalman filter is the most optimized linear filter, it is used in many engineering fields. This article introduced the application of Kalman filter as the loop filter of the carrier tracking loop in GPS receiver, to improve tracking sensitivity. The traditional loop filter is replaced. Simulation results show that the new structure improves the tracking sensitivity by 6dB and can make the tracking loop more robust when the navigation signal is languishing. The optimization of theKalman filter is also analysed, which further improves the sensitivity by 4dB.

HMM filtering and parameter estimation of an electricity spot price model

International Nuclear Information System (INIS)

Erlwein, Christina; Benth, Fred Espen; Mamon, Rogemar

2010-01-01

In this paper we develop a model for electricity spot price dynamics. The spot price is assumed to follow an exponential Ornstein-Uhlenbeck (OU) process with an added compound Poisson process. In this way, the model allows for mean-reversion and possible jumps. All parameters are modulated by a hidden Markov chain in discrete time. They are able to switch between different economic regimes representing the interaction of various factors. Through the application of reference probability technique, adaptive filters are derived, which in turn, provide optimal estimates for the state of the Markov chain and related quantities of the observation process. The EM algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters. We implement this self-calibrating model on a deseasonalised series of daily spot electricity prices from the Nordic exchange Nord Pool. On the basis of one-step ahead forecasts, we found that the model is able to capture the empirical characteristics of Nord Pool spot prices. (author)

DYNAMIC ESTIMATION FOR PARAMETERS OF INTERFERENCE SIGNALS BY THE SECOND ORDER EXTENDED KALMAN FILTERING

Directory of Open Access Journals (Sweden)

P. A. Ermolaev

2014-03-01

Full Text Available Data processing in the interferometer systems requires high-resolution and high-speed algorithms. Recurrence algorithms based on parametric representation of signals execute consequent processing of signal samples. In some cases recurrence algorithms make it possible to increase speed and quality of data processing as compared with classic processing methods. Dependence of the measured interferometer signal on parameters of its model and stochastic nature of noise formation in the system is, in general, nonlinear. The usage of nonlinear stochastic filtering algorithms is expedient for such signals processing. Extended Kalman filter with linearization of state and output equations by the first vector parameters derivatives is an example of these algorithms. To decrease approximation error of this method the second order extended Kalman filtering is suggested with additionally usage of the second vector parameters derivatives of model equations. Examples of algorithm implementation with the different sets of estimated parameters are described. The proposed algorithm gives the possibility to increase the quality of data processing in interferometer systems in which signals are forming according to considered models. Obtained standard deviation of estimated amplitude envelope does not exceed 4% of the maximum. It is shown that signal-to-noise ratio of reconstructed signal is increased by 60%.

Sensitivity analysis of railpad parameters on vertical railway track dynamics

NARCIS (Netherlands)

Oregui Echeverria-Berreyarza, M.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.

2016-01-01

This paper presents a sensitivity analysis of railpad parameters on vertical railway track dynamics, incorporating the nonlinear behavior of the fastening (i.e., downward forces compress the railpad whereas upward forces are resisted by the clamps). For this purpose, solid railpads, rail-railpad

On detection of black hole quasinormal ringdowns: Detection efficiency and waveform parameter determination in matched filtering

International Nuclear Information System (INIS)

Tsunesada, Yoshiki; Tatsumi, Daisuke; Kanda, Nobuyuki; Nakano, Hiroyuki; Ando, Masaki; Sasaki, Misao; Tagoshi, Hideyuki; Takahashi, Hirotaka

2005-01-01

Gravitational radiation from a slightly distorted black hole with ringdown waveform is well understood in general relativity. It provides a probe for direct observation of black holes and determination of their physical parameters, masses and angular momenta (Kerr parameters). For ringdown searches using data of gravitational wave detectors, matched filtering technique is useful. In this paper, we describe studies on problems in matched filtering analysis in realistic gravitational wave searches using observational data. Above all, we focus on template constructions, matches or signal-to-noise ratios (SNRs), detection probabilities for Galactic events, and accuracies in evaluation of waveform parameters or black hole hairs. In template design for matched filtering, search parameter ranges and template separations are determined by requirements from acceptable maximum loss of SNRs, detection efficiencies, and computational costs. In realistic searches using observational data, however, effects of nonstationary noises cause decreases of SNRs, and increases of errors in waveform parameter determinations. These problems will potentially arise in any matched filtering searches for any kind of waveforms. To investigate them, we have performed matched filtering analysis for artificial ringdown signals which are generated with Monte-Carlo technique and injected into the TAMA300 observational data. We employed an efficient method to construct a bank of ringdown filters recently proposed by Nakano et al., and use a template bank generated from a criterion such that losses of SNRs of any signals do not exceed 2%. We found that this criterion is fulfilled in ringdown searches using TAMA300 data, by examining distribution of SNRs of simulated signals. It is also shown that with TAMA300 sensitivity, the detection probability for Galactic ringdown events is about 50% for black holes of masses greater than 20M · with SNR>10. The accuracies in waveform parameter estimations are

Box-particle probability hypothesis density filtering

OpenAIRE

Schikora, M.; Gning, A.; Mihaylova, L.; Cremers, D.; Koch, W.

2014-01-01

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-p...

Kalman filters for assimilating near-surface observations into the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

Science.gov (United States)

Medina, H.; Romano, N.; Chirico, G. B.

2014-07-01

This study presents a dual Kalman filter (DSUKF - dual standard-unscented Kalman filter) for retrieving states and parameters controlling the soil water dynamics in a homogeneous soil column, by assimilating near-surface state observations. The DSUKF couples a standard Kalman filter for retrieving the states of a linear solver of the Richards equation, and an unscented Kalman filter for retrieving the parameters of the soil hydraulic functions, which are defined according to the van Genuchten-Mualem closed-form model. The accuracy and the computational expense of the DSUKF are compared with those of the dual ensemble Kalman filter (DEnKF) implemented with a nonlinear solver of the Richards equation. Both the DSUKF and the DEnKF are applied with two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil water matric pressure head (h). The comparison analyses are conducted with reference to synthetic time series of the true states, noise corrupted observations, and synthetic time series of the meteorological forcing. The performance of the retrieval algorithms are examined accounting for the effects exerted on the output by the input parameters, the observation depth and assimilation frequency, as well as by the relationship between retrieved states and assimilated variables. The uncertainty of the states retrieved with DSUKF is considerably reduced, for any initial wrong parameterization, with similar accuracy but less computational effort than the DEnKF, when this is implemented with ensembles of 25 members. For ensemble sizes of the same order of those involved in the DSUKF, the DEnKF fails to provide reliable posterior estimates of states and parameters. The retrieval performance of the soil hydraulic parameters is strongly affected by several factors, such as the initial guess of the unknown parameters, the wet or dry

Robust track fitting in the Belle II inner tracking detector

International Nuclear Information System (INIS)

Nadler, Moritz; Frühwirth, Rudolf

2012-01-01

Track fitting in the new inner tracker of the Belle II experiment uses the GENFIT package. In the latter both a standard Kalman filter and a robust extension, the deterministic annealing filter (DAF), are implemented. This contribution presents the results of a simulation experiment which examines the performance of the DAF in the inner tracker, in terms of outlier detection ability and of the impact of different kinds of background on the quality of the fitted tracks.

Target Response Adaptation for Correlation Filter Tracking

KAUST Repository

Bibi, Adel Aamer; Mueller, Matthias; Ghanem, Bernard

2016-01-01

Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations

Thermal Tracking of Sports Players

Directory of Open Access Journals (Sweden)

Rikke Gade

2014-07-01

Full Text Available We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline multi-target tracking algorithm. Experiments are performed on a manually annotated 2-minutes video sequence of a real soccer game. The Kalman filter shows a very promising result on this rather challenging sequence with a tracking accuracy above 70% and is superior compared with the offline tracking approach. Furthermore, the combined detection and tracking algorithm runs in real time at 33 fps, even with large image sizes of 1920 × 480 pixels.

Thermal tracking of sports players.

Science.gov (United States)

Gade, Rikke; Moeslund, Thomas B

2014-07-29

We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline multi-target tracking algorithm. Experiments are performed on a manually annotated 2-minutes video sequence of a real soccer game. The Kalman filter shows a very promising result on this rather challenging sequence with a tracking accuracy above 70% and is superior compared with the offline tracking approach. Furthermore, the combined detection and tracking algorithm runs in real time at 33 fps, even with large image sizes of 1920 × 480 pixels.

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

Science.gov (United States)

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-08-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

An efficient central DOA tracking algorithm for multiple incoherently distributed sources

Science.gov (United States)

Hassen, Sonia Ben; Samet, Abdelaziz

2015-12-01

In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.

Bottom loaded filter for radioactive liquid

International Nuclear Information System (INIS)

Wendland, W.G.

1980-01-01

A specification is given for a bottom loaded filter assembly for filtering radioactive liquids through a replaceable cartridge filter, which includes a lead-filled jacket enveloping a housing having a chamber therein for the filter cartridge. A track arrangement carries a hatch for sealing the chamber. A spacer plug supports the cartridge within guide means associated with the inlet conduit in the chamber. The plug and cartridge drop out of the chamber when the hatch is unbolted and moved laterally of the chamber along the track. During cartridge replacement a new plug and cartridge are supported in the guide means by a spacer bar inserted across the track means under the chamber. The hatch is then slid under the chamber and bolted to a flange on the housing, engaging an O-ring to seal the chamber. (author)

Unscented Kalman filtering for articulated human tracking

DEFF Research Database (Denmark)

Boesen Lindbo Larsen, Anders; Hauberg, Søren; Pedersen, Kim Steenstrup

2011-01-01

We present an articulated tracking system working with data from a single narrow baseline stereo camera. The use of stereo data allows for some depth disambiguation, a common issue in articulated tracking, which in turn yields likelihoods that are practically unimodal. While current state...... with superior results. Tracking quality is measured by comparing with ground truth data from a marker-based motion capture system....

Optimal Control Method of Robot End Position and Orientation Based on Dynamic Tracking Measurement

Science.gov (United States)

Liu, Dalong; Xu, Lijuan

2018-01-01

In order to improve the accuracy of robot pose positioning and control, this paper proposed a dynamic tracking measurement robot pose optimization control method based on the actual measurement of D-H parameters of the robot, the parameters is taken with feedback compensation of the robot, according to the geometrical parameters obtained by robot pose tracking measurement, improved multi sensor information fusion the extended Kalan filter method, with continuous self-optimal regression, using the geometric relationship between joint axes for kinematic parameters in the model, link model parameters obtained can timely feedback to the robot, the implementation of parameter correction and compensation, finally we can get the optimal attitude angle, realize the robot pose optimization control experiments were performed. 6R dynamic tracking control of robot joint robot with independent research and development is taken as experimental subject, the simulation results show that the control method improves robot positioning accuracy, and it has the advantages of versatility, simplicity, ease of operation and so on.

Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

Science.gov (United States)

Medina, H.; Romano, N.; Chirico, G. B.

2012-12-01

We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

Study of heat treatment parameters for large-scale hydraulic steel gate track

Directory of Open Access Journals (Sweden)

Ping-zhou Cao

2013-10-01

Full Text Available In order to enhance external hardness and strength, a large-scale hydraulic gate track should go through heat treatment. The current design method of hydraulic gate wheels and tracks is based on Hertz contact linear elastic theory, and does not take into account the changes in mechanical properties of materials caused by heat treatment. In this study, the heat treatment parameters were designed and analyzed according to the bearing mechanisms of the wheel and track. The quenching process of the track was simulated by the ANSYS program, and the temperature variation, residual stress, and deformation were obtained and analyzed. The metallurgical structure field after heat treatment was predicted by the method based on time-temperature-transformation (TTT curves. The results show that the analysis method and designed track heat treatment process are feasible, and can provide a reference for practical projects.

Experimental study of filter cake formation on different filter media

International Nuclear Information System (INIS)

Saleem, M.

2009-01-01

Removal of particulate matter from gases generated in the process industry is important for product recovery as well as emission control. Dynamics of filtration plant depend on operating conditions. The models, that predict filter plant behaviour, involve empirical resistance parameters which are usually derived from limited experimental data and are characteristics of the filter media and filter cake (dust deposited on filter medium). Filter cake characteristics are affected by the nature of filter media, process parameters and mode of filter regeneration. Removal of dust particles from air is studied in a pilot scale jet pulsed bag filter facility resembling closely to the industrial filters. Limestone dust and ambient air are used in this study with two widely different filter media. All important parameters like pressure drop, gas flow rate, dust settling, are recorded continuously at 1s interval. The data is processed for estimation of the resistance parameters. The pressure drop rise on test filter media is compared. Results reveal that the surface of filter media has an influence on pressure drop rise (concave pressure drop rise). Similar effect is produced by partially jet pulsed filter surface. Filter behaviour is also simulated using estimated parameters and a simplified model and compared with the experimental results. Distribution of cake area load is therefore an important aspect of jet pulse cleaned bag filter modeling. Mean specific cake resistance remains nearly constant on thoroughly jet pulse cleaned membrane coated filter bags. However, the trend can not be confirmed without independent cake height and density measurements. Thus the results reveal the importance of independent measurements of cake resistance. (author)

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

Science.gov (United States)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

Impact of the genfit2 Kalman-filter-based algorithms on physics simulations performed with PandaRoot

Energy Technology Data Exchange (ETDEWEB)

Prencipe, Elisabetta; Ritman, James [Forschungszentrum Juelich, IKP1, Juelich (Germany); Collaboration: PANDA-Collaboration

2016-07-01

PANDA is a planned experiment at FAIR (Darmstadt) with a cooled antiproton beam in a range [1.5;15] GeV/c, allowing a wide physics program in nuclear and particle physics. It is the only experiment worldwide, which combines a solenoid field (B=2 T) and a dipole field (B=2 Tm) in an experiment with a fixed target topology, in that energy regime. The tracking system of PANDA involves the presence of a high performance silicon vertex detector, a GEM detector, a Straw-Tubes central tracker, a forward tracking system, and a luminosity monitor. The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FAIRRoot project. The algorithm here presented is based on a tool containing the Kalman Filter equations and a deterministic annealing filter (genfit). The Kalman-Filter-based algorithms have a wide range of applications; among those in particle physics they can perform extrapolations of track parameters and covariance matrices. The impact on physics simulations performed for the PANDA experiment is shown for the first time, with the PandaRoot framework: improvement is shown for those channels where a good low momentum tracking is required (p{sub T}<400 MeV/c), i.e. D mesons and Λ reconstruction, of about a factor 2.

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Directory of Open Access Journals (Sweden)

Hongxiao Yu

2015-05-01

Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

Rotational Kinematics Model Based Adaptive Particle Filter for Robust Human Tracking in Thermal Omnidirectional Vision

Directory of Open Access Journals (Sweden)

Yazhe Tang

2015-01-01

Full Text Available This paper presents a novel surveillance system named thermal omnidirectional vision (TOV system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.

A New Filtering Algorithm Utilizing Radial Velocity Measurement

Institute of Scientific and Technical Information of China (English)

LIU Yan-feng; DU Zi-cheng; PAN Quan

2005-01-01

Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.

Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

Joint Multi-Fiber NODDI Parameter Estimation and Tractography using the Unscented Information Filter

Directory of Open Access Journals (Sweden)

Yogesh eRathi

2016-04-01

Full Text Available Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF. Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters, which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

Extended Kalman Filtering to estimate temperature and irradiation for maximum power point tracking of a photovoltaic module

International Nuclear Information System (INIS)

Docimo, D.J.; Ghanaatpishe, M.; Mamun, A.

2017-01-01

This paper develops an algorithm for estimating photovoltaic (PV) module temperature and effective irradiation level. The power output of a PV system depends directly on both of these states. Estimating the temperature and irradiation allows for improved state-based control methods while eliminating the need of additional sensors. Thermal models and irradiation estimators have been developed in the literature, but none incorporate feedback for estimation. This paper outlines an Extended Kalman Filter for temperature and irradiation estimation. These estimates are, in turn, used within a novel state-based controller that tracks the maximum power point of the PV system. Simulation results indicate this state-based controller provides up to an 8.5% increase in energy produced per day as compared to an impedance matching controller. A sensitivity analysis is provided to examine the impact state estimate errors have on the ability to find the optimal operating point of the PV system. - Highlights: • Developed a temperature and irradiation estimator for photovoltaic systems. • Designed an Extended Kalman Filter to handle model and measurement uncertainty. • Developed a state-based controller for maximum power point tracking (MPPT). • Validated combined estimator/controller algorithm for different weather conditions. • Algorithm increases energy captured up to 8.5% over traditional MPPT algorithms.

Perspectives on Nonlinear Filtering

KAUST Repository

Law, Kody

2015-01-01

The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).

Perspectives on Nonlinear Filtering

KAUST Repository

Law, Kody

2015-01-07

The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).

An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

Institute of Scientific and Technical Information of China (English)

Xiaogu ZHENG

2009-01-01

An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.

Radionuclide release rate inversion of nuclear accidents in nuclear facility based on Kalman filter

International Nuclear Information System (INIS)

Tang Xiuhuan; Bao Lihong; Li Hua; Wan Junsheng

2014-01-01

The rapidly and continually back-calculating source term is important for nuclear emergency response. The Gaussian multi-puff atmospheric dispersion model was used to produce regional environment monitoring data virtually, and then a Kalman filter was designed to inverse radionuclide release rate of nuclear accidents in nuclear facility and the release rate tracking in real time was achieved. The results show that the Kalman filter combined with Gaussian multi-puff atmospheric dispersion model can successfully track the virtually stable, linear or nonlinear release rate after being iterated about 10 times. The standard error of inversion results increases with the true value. Meanwhile extended Kalman filter cannot inverse the height parameter of accident release as interceptive error is too large to converge. Kalman filter constructed from environment monitoring data and Gaussian multi-puff atmospheric dispersion model can be applied to source inversion in nuclear accident which is characterized by static height and position, short and continual release in nuclear facility. Hence it turns out to be an alternative source inversion method in nuclear emergency response. (authors)

Bottom loaded filter for radioactive liquids

International Nuclear Information System (INIS)

Wendland, W.G.

1980-01-01

A bottom loaded filter assembly for filtering radioactive liquids through a replaceable cartridge filter is disclosed. The filter assembly includes a lead-filled jacket enveloping a housing having a chamber therein for the filter cartridge. A track arrangement carries a hatch for sealing the chamber. A spacer plug supports the cartridge within guide means associated with the inlet conduit in the chamber. The plug and cartridge drop out of the chamber when the hatch is unbolted and move laterally of the chamber. During cartridge replacement, a new plug and cartridge are supported in the guide means by a spacer bar inserted across the track means under the chamber. The hatch is then slid under the chamber and bolted to the vessel, engaging an o-ring to seal the chamber

Application of Knowledge-Based Techniques to Tracking Function

National Research Council Canada - National Science Library

Farina, A

2006-01-01

...: historical survey of stochastic filtering theory; overview of tracking systems with some details on mono-sensor and multi-sensor tracking, evolution of filtering logics, evolution of correlation logics, and presentation of recent findings on non...

Off-line tracking of series parameters in distribution systems using AMI data

Energy Technology Data Exchange (ETDEWEB)

Williams, Tess L.; Sun, Yannan; Schneider, Kevin

2016-05-01

Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimation procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.

Physichal parameters for wedge filters used in radiotherapy

International Nuclear Information System (INIS)

Strunga, Emil

1995-01-01

Wedge filters using in radiotherapy up two important problems: attenuation of gamma rays introduced by the presence of wedge filters and spinning of isodoses curves plate. Depending of irradiation geometry, characterised by D w , - source filter distance, D c - source dose's estimate point distance, a - side of irradiation field; nature and size filter: α - wedge angle, μ - linear adsorption coefficient, ε - filter cover attenuation w - filter side, and nature of target volume characterised by μ' - linear absorption coefficient of medium has been estimated absorption factor of wedge filter (k w ) for two irradiation geometry: and spinning angle of isodose plate (Θ): 3) tg θ (μD w (μ'D c - 2 Calculated values has been compared with the experimental measured values, for a cobaltotherapy unit Rokus-M, and the result was that between the two series of dates it is a good concordance

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

Science.gov (United States)

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Fuzzy Vector Field Orientation Feedback Control-Based Slip Compensation for Trajectory Tracking Control of a Four Track Wheel Skid-steered Mobile Robot

Directory of Open Access Journals (Sweden)

Xuan Vinh Ha

2013-04-01

Full Text Available Skid-steered mobile robots have been widely used in exploring unknown environments and in military applications. In this paper, the tuning fuzzy Vector Field Orientation (FVFO feedback control method is proposed for a four track wheel skid-steered mobile robot (4-TW SSMR using flexible fuzzy logic control (FLC. The extended Kalman filter is utilized to estimate the positions, velocities and orientation angles, which are used for feedback control signals in the FVFO method, based on the AHRS kinematic motion model and velocity constraints. In addition, in light of the wheel slip and the braking ability of the robot, we propose a new method for estimating online wheel slip parameters based on a discrete Kalman filter to compensate for the velocity constraints. As demonstrated by our experimental results, the advantages of the combination of the proposed FVFO and wheel slip estimation methods overcome the limitations of the others in the trajectory tracking control problem for a 4-TW SSMR.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters.

Science.gov (United States)

Hadwin, Paul J; Peterson, Sean D

2017-04-01

The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.

Robust Visual Tracking Using the Bidirectional Scale Estimation

Directory of Open Access Journals (Sweden)

An Zhiyong

2017-01-01

Full Text Available Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique

Science.gov (United States)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.

Comparison of the dosimetric parameters in linear accelerators with flattening filter-free (FFF) and flattening filter (FF)

International Nuclear Information System (INIS)

Souza, Anderson S.; Rostelato, Maria Elisa C.M.; Zeituni, Carlos A.; Moura, Eduardo S.; Rodrigues, Bruna T.; Souza, Daiane C.; Tiezzi, Rodrigo; Souza, Carla D.; Melo, Emerson R.; Camargo, Anderson R.; Batista, Talita Q.

2015-01-01

This paper discusses the main features associated with the dosimetric parameters between FFF and FF Linacs. A set of Varian TrueBeam Linac and Varian 23EX dosimetric measurements was acquired to perform the experimental measurements. The dose measurements were carried out in a water Blue phantom, with a waterproof ionization chambers: farmer ionization chamber (0.6 cm 3 ) and Exradin A1SL(0.053 cm 3 ) , for fields 5 x 5, 8 x 8, 10 x 10, 15 x 15, 30 x 30 cm 2 . The 6 MV FFF and FF was the energy used in this work. Percent Depth Dose (PDD) was the dosimetric parameters evaluated using a fixed Source Surface Distance of 100 cm. One depth were applied for the measurements, 10 cm (central axis) from the water surface. The 6 MV FFF showed less penetrating than the 6 MV FF. This is due to the removal flattening filter causes more lower energy photons on the central axis. The field sizes were equivalent for both FFF and FF. The main advantage in operate linear accelerators without flattening filter is due to the high doses rates delivered during the treatment. High doses rates could reduce the patient treatment time and may be beneficial for some treatment techniques such as IMRT and SRT. (author)

An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis

Directory of Open Access Journals (Sweden)

Qihang Wang

2016-07-01

Full Text Available In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.

Research on the Effects of Hydropneumatic Parameters on Tracked Vehicle Ride Safety Based on Cosimulation

Directory of Open Access Journals (Sweden)

Shousong Han

2017-01-01

Full Text Available Ride safety of a tracked vehicle is the key focus of this research. The factors that affect the ride safety of a vehicle are analyzed and evaluation parameters with their criteria are proposed. A multibody cosimulation approach is used to investigate the effects of hydropneumatic parameters on the ride safety and aid with design optimization and tuning of the suspension system. Based on the cosimulation environment, the vehicle multibody dynamics (MBD model and the road model are developed using RecurDyn, which is linked to the hydropneumatic suspension model developed in Lab AMESim. Test verification of a single suspension unit is accomplished and the suspension parameters are implemented within the hydropneumatic model. Virtual tests on a G class road at different speeds are conducted. Effects of the accumulator charge pressure, damping diameter, and the track tensioning pressure on the ride safety are analyzed and quantified. This research shows that low accumulator charge pressure, improper damping diameter, and insufficient track tensioning pressure will deteriorate the ride safety. The results provide useful references for the optimal design and control of the parameters of a hydropneumatic suspension.

Automatic detection, tracking and sensor integration

Science.gov (United States)

Trunk, G. V.

1988-06-01

This report surveys the state of the art of automatic detection, tracking, and sensor integration. In the area of detection, various noncoherent integrators such as the moving window integrator, feedback integrator, two-pole filter, binary integrator, and batch processor are discussed. Next, the three techniques for controlling false alarms, adapting thresholds, nonparametric detectors, and clutter maps are presented. In the area of tracking, a general outline is given of a track-while-scan system, and then a discussion is presented of the file system, contact-entry logic, coordinate systems, tracking filters, maneuver-following logic, tracking initiating, track-drop logic, and correlation procedures. Finally, in the area of multisensor integration the problems of colocated-radar integration, multisite-radar integration, radar-IFF integration, and radar-DF bearing strobe integration are treated.

Capturing Revolute Motion and Revolute Joint Parameters with Optical Tracking

Science.gov (United States)

Antonya, C.

2017-12-01

Optical tracking of users and various technical systems are becoming more and more popular. It consists of analysing sequence of recorded images using video capturing devices and image processing algorithms. The returned data contains mainly point-clouds, coordinates of markers or coordinates of point of interest. These data can be used for retrieving information related to the geometry of the objects, but also to extract parameters for the analytical model of the system useful in a variety of computer aided engineering simulations. The parameter identification of joints deals with extraction of physical parameters (mainly geometric parameters) for the purpose of constructing accurate kinematic and dynamic models. The input data are the time-series of the marker’s position. The least square method was used for fitting the data into different geometrical shapes (ellipse, circle, plane) and for obtaining the position and orientation of revolute joins.

Generalised Filtering

Directory of Open Access Journals (Sweden)

Karl Friston

2010-01-01

Full Text Available We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying states and time-invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free-energy bound on the model's log-evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed-form variational version, and conclude with an illustrative application to a nonlinear state-space model of brain imaging time-series.

A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

KAUST Repository

El Gharamti, Mohamad; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2015-01-01

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation

A novel generic framework for track fitting in complex detector systems

International Nuclear Information System (INIS)

Hoeppner, C.; Neubert, S.; Ketzer, B.; Paul, S.

2010-01-01

This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices through the experiment are also implemented as interchangeable modules. Different track parameterizations and extrapolation routines can be used simultaneously for fitting of the same physical track. Representations of detector hits are the third modular ingredient to the framework. The hit dimensionality and orientation of planar tracking detectors are not restricted. Tracking information from detectors which do not measure the passage of particles in a fixed physical detector plane, e.g. drift chambers or TPCs, is used without any simplification. The concept is implemented in a light-weight C++ library called GENFIT, which is available as free software.

A novel generic framework for track fitting in complex detector systems

Energy Technology Data Exchange (ETDEWEB)

Hoeppner, C., E-mail: christian.hoeppner@cern.c [Technische Universitaet Muenchen, Physik Department, 85748 Garching (Germany); Neubert, S.; Ketzer, B.; Paul, S. [Technische Universitaet Muenchen, Physik Department, 85748 Garching (Germany)

2010-08-21

This paper presents a novel framework for track fitting which is usable in a wide range of experiments, independent of the specific event topology, detector setup, or magnetic field arrangement. This goal is achieved through a completely modular design. Fitting algorithms are implemented as interchangeable modules. At present, the framework contains a validated Kalman filter. Track parameterizations and the routines required to extrapolate the track parameters and their covariance matrices through the experiment are also implemented as interchangeable modules. Different track parameterizations and extrapolation routines can be used simultaneously for fitting of the same physical track. Representations of detector hits are the third modular ingredient to the framework. The hit dimensionality and orientation of planar tracking detectors are not restricted. Tracking information from detectors which do not measure the passage of particles in a fixed physical detector plane, e.g. drift chambers or TPCs, is used without any simplification. The concept is implemented in a light-weight C++ library called GENFIT, which is available as free software.

Box-particle intensity filter

OpenAIRE

Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila; Cremers, Daniel; Koch, Wofgang; Streit, Roy

2012-01-01

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables thi...

Enhanced online convolutional neural networks for object tracking

Science.gov (United States)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

The Application of Barnes Filter to Positioning the Center of Landed Tropical Cyclone in Numerical Models

Directory of Open Access Journals (Sweden)

Haibo Zou

2018-01-01

Full Text Available After a tropical cyclone (TC making landfall, the numerical model output sea level pressure (SLP presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85% the DDs are small (between −30 km and 30 km, while in a few cases (about 15% the DDs are large (greater than 30 km even 70 km. This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.

Fusing inertial sensor data in an extended Kalman filter for 3D camera tracking.

Science.gov (United States)

Erdem, Arif Tanju; Ercan, Ali Özer

2015-02-01

In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.

Thermal Tracking of Sports Players

DEFF Research Database (Denmark)

Gade, Rikke; Moeslund, Thomas B.

2014-01-01

We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline...

Fast Filter Central Drift Chamber Program. MAC Note 404

International Nuclear Information System (INIS)

Ford, W.T.

1979-01-01

A fast filter of the central detector tracking program was developed and tested. The search for tracks is conducted as in the full linking program - from the outermost layer hit to the next layer in. Any hit is accepted within a road centered which is large enough to allow for stero displacement and any curvature. With the fast filter, this is continued to the innermost layer, with five or more hits accepted as a track

Low Complexity Track Initialization from a Small Set of Non-Invertible Measurements

Directory of Open Access Journals (Sweden)

Wolfgang Koch

2008-02-01

Full Text Available Target tracking from non-invertible measurement sets, for example, incomplete spherical coordinates measured by asynchronous sensors in a sensor network, is a task of data fusion present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by poor initialization. Instead of using high complexity numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations. This directly pertains to multi-hypothesis tracking (MHT, where in the presence of clutter and/or multiple targets (i low complexity algorithms are desirable and (ii using a small set of measurements avoids the combinatorial explosion. Our approach uses no numerical optimization, simply evaluating several equations to find the state estimates. This is possible since we avoid an over-determined setup by initializing only from the minimum necessary subset of measurements. Loss in accuracy is minimized by choosing the best subset using an optimality criterion and incorporating the leftover measurements afterwards. Additionally, we provide the possibility to estimate only sub-sets of parameters, and to reliably model the resulting added uncertainties by the covariance matrix. We compare two different implementations, differing in the approximation of the posterior: linearizing the measurement equation as in the extended Kalman filter (EKF or employing the unscented transform (UT. The approach will be studied in two practical examples: 3D track initialization using bearingsonly measurements or using slant-range and azimuth only.

Pose Tracking Algorithm of an Endoscopic Surgery Robot Wrist

International Nuclear Information System (INIS)

Wang, L; Yin, H L; Meng, Q

2006-01-01

In recent two decades, more and more research on the endoscopic surgery has been carried out [2]. Most of the work focuses on the development of the robot in the field of robotics and the navigation of the surgery tools based on computer graphics. But the tracking and locating of the EndoWrist is also a very important aspect. This paper deals with the the tracking algorithm of the EndoWrist's pose (position and orientation). The linear tracking of the position is handled by the Kalman Filter. The quaternion-based nonlinear orientation tracking is implemented with the Extended Kalman Filter. The most innovative point of this paper is the parameterization of the motion model of the Extended Kalman Filter

Parameter Deduction and Accuracy Analysis of Track Beam Curves in Straddle-type Monorail Systems

Directory of Open Access Journals (Sweden)

Xiaobo Zhao

2015-12-01

Full Text Available The accuracy of the bottom curve of a PC track beam is strongly related to the production quality of the entire beam. Many factors may affect the parameters of the bottom curve, such as the superelevation of the curve and the deformation of a PC track beam. At present, no effective method has been developed to determine the bottom curve of a PC track beam; therefore, a new technique is presented in this paper to deduce the parameters of such a curve and to control the accuracy of the computation results. First, the domain of the bottom curve of a PC track beam is assumed to be a spindle plane. Then, the corresponding supposed top curve domain is determined based on a geometrical relationship that is the opposite of that identified by the conventional method. Second, several optimal points are selected from the supposed top curve domain according to the dichotomy algorithm; the supposed top curve is thus generated by connecting these points. Finally, one rigorous criterion is established in the fractal dimension to assess the accuracy of the assumed top curve deduced in the previous step. If this supposed curve coincides completely with the known top curve, then the assumed bottom curve corresponding to the assumed top curve is considered to be the real bottom curve. This technique of determining the bottom curve of a PC track beam is thus proven to be efficient and accurate.

A Method of Time-Varying Rayleigh Channel Tracking in MIMO Radio System

Institute of Scientific and Technical Information of China (English)

GONG Yan-fei; HE Zi-shu; HAN Chun-lin

2005-01-01

A method of MIMO channel tracking based on Kalman filter and MMSE-DFE is proposed. The Kalman filter tracks the time-varying channel by using the MMSE-DFE decision and the MMSE-DFE conducts the next decision by using the channel estimates produced by the Kalman filter. Polynomial fitting is used to bridge the gap between the channel estimates produced by the Kalman filter and those needed for the DFE decision. Computer simulation demonstrates that this method can track the MIMO time-varying channel effectively.

Model Adaptation for Prognostics in a Particle Filtering Framework

Directory of Open Access Journals (Sweden)

Bhaskar Saha

2011-01-01

Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Model Adaptation for Prognostics in a Particle Filtering Framework

Science.gov (United States)

Saha, Bhaskar; Goebel, Kai Frank

2011-01-01

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Modified temporal approach to meta-optimizing an extended Kalman filter's parameters

CSIR Research Space (South Africa)

Salmon

2014-07-01

Full Text Available stream_source_info Salmon_2014.pdf.txt stream_content_type text/plain stream_size 1233 Content-Encoding UTF-8 stream_name Salmon_2014.pdf.txt Content-Type text/plain; charset=UTF-8 2014 IEEE International Geoscience... and Remote Sensing Symposium, Québec, Canada, 13-18 July 2014 A modified temporal approach to meta-optimizing an Extended Kalman Filter's parameters B. P. Salmon ; W. Kleynhans ; J. C. Olivier ; W. C. Olding ; K. J. Wessels ; F. van den Bergh...

Production parameters for the formation of metallic nanotubules in etched tracks

International Nuclear Information System (INIS)

Fink, D.; Petrov, A.V.; Rao, V.; Wilhelm, M.; Demyanov, S.; Szimkowiak, P.; Behar, M.; Alegaonkar, P.S.; Chadderton, L.T.

2003-01-01

The formation of conducting nanotubules in etched tracks is reported in literature since about a decade. However, up to now precise production recipes are scarce. For this sake we present here a systematic study on some important factors that influence the formation of metallic nanotubules. In the case of chemical deposition, the first question to be answered is the choice of the activation technique to produce the required activation centers. Both the time of activation and the time of subsequent chemical deposition are crucial parameters in this connection. Finally, the maximum temperature is determined up to which thermal stability of the etched tracks and of the tubules therein is given. This study should allow one to predict better the efficiency of conducting nanotubule formation

Measurement of track opening contours of oblique incident 4He and 7Li-ions in CR-39: Relevance for calculation of track formation parameters

International Nuclear Information System (INIS)

Hermsdorf, D.; Reichelt, U.

2010-01-01

Solid State Nuclear Track Detectors (SSNTD) irradiated in realistic radiation fields exhibits after chemical etching very complex track images resulting from different species of particles and their energy spectra and randomly distributed angles of incidence or emission. Reading out such an etched detector surface with a light microscope, quite different track opening contours are observed. Beside the number of tracks, typically their major and minor axes are measured. In this work following problems arising from such experimental situations will be investigated: ·the measurement of track contour parameters for oblique incident 4 He and 7 Li-ions of different energies and angles in CR-39 detectors ·the theoretical description of the angular variation of both axes. ·the possibility to extract physical and spectroscopic information from major and minor track axes. This analysis is based on an intensive experimental program and the comprehensive study of theoretical models available for description of track revealing processes in CR-39.

A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics

Directory of Open Access Journals (Sweden)

Joaquín Míguez

2004-11-01

Full Text Available In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.

Kalman Filter Predictor and Initialization Algorithm for PRI Tracking

National Research Council Canada - National Science Library

Hock, Melinda

1998-01-01

.... The algorithm uses a Kalman filter for prediction combined with a preprocessing routine to determine the period of the stagger sequence and to construct an uncorrupted data set for Kalman filter initialization...

Estimating volatility and model parameters of stochastic volatility models with jumps using particle filter

NARCIS (Netherlands)

Aihara, ShinIchi; Bagchi, Arunabha; Saha, S.

Despite the success of particle filter, there are two factors which cause difficulties in its implementation. The first one is the choice of importance functions commonly used in the literature which are far from being optimal. The second one is the combined state and parameter estimation problem.

Improvement of the fringe analysis algorithm for wavelength scanning interferometry based on filter parameter optimization.

Science.gov (United States)

Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian

2018-03-20

The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.

An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models

KAUST Repository

Gharamti, M. E.

2015-05-11

The ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model

Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter

Directory of Open Access Journals (Sweden)

J. D. Annan

2005-01-01

Full Text Available We demonstrate the application of an efficient multivariate probabilistic parameter estimation method to a spectral primitive equation atmospheric GCM. The method, which is based on the Ensemble Kalman Filter, is effective at tuning the surface air temperature climatology of the model to both identical twin data and reanalysis data. When 5 parameters were simultaneously tuned to fit the model to reanalysis data, the model errors were reduced by around 35% compared to those given by the default parameter values. However, the precipitation field proved to be insensitive to these parameters and remains rather poor. The model is computationally cheap but chaotic and otherwise realistic, and the success of these experiments suggests that this method should be capable of tuning more sophisticated models, in particular for the purposes of climate hindcasting and prediction. Furthermore, the method is shown to be useful in determining structural deficiencies in the model which can not be improved by tuning, and so can be a useful tool to guide model development. The work presented here is for a limited set of parameters and data, but the scalability of the method is such that it could easily be extended to a more comprehensive parameter set given sufficient observational data to constrain them.

Pose Tracking Algorithm of an Endoscopic Surgery Robot Wrist

Energy Technology Data Exchange (ETDEWEB)

Wang, L [Chinese-German Institute of Automatic Control Engineering, Tongji University (China); Yin, H L [Chinese-German Institute of Automatic Control Engineering, Tongji University (China); Meng, Q [Shanghai University of Electric Power (China)

2006-10-15

In recent two decades, more and more research on the endoscopic surgery has been carried out [2]. Most of the work focuses on the development of the robot in the field of robotics and the navigation of the surgery tools based on computer graphics. But the tracking and locating of the EndoWrist is also a very important aspect. This paper deals with the the tracking algorithm of the EndoWrist's pose (position and orientation). The linear tracking of the position is handled by the Kalman Filter. The quaternion-based nonlinear orientation tracking is implemented with the Extended Kalman Filter. The most innovative point of this paper is the parameterization of the motion model of the Extended Kalman Filter.

PolarTrack: Optical Outside-In Device Tracking that Exploits Display Polarization

DEFF Research Database (Denmark)

Rädle, Roman; Jetter, Hans-Christian; Fischer, Jonathan

2018-01-01

PolarTrack is a novel camera-based approach to detecting and tracking mobile devices inside the capture volume. In PolarTrack, a polarization filter continuously rotates in front of an off-the-shelf color camera, which causes the displays of observed devices to periodically blink in the camera feed....... The periodic blinking results from the physical characteristics of current displays, which shine polarized light either through an LC overlay to produce images or through a polarizer to reduce light reflections on OLED displays. PolarTrack runs a simple detection algorithm on the camera feed to segment...... displays and track their locations and orientations, which makes PolarTrack particularly suitable as a tracking system for cross-device interaction with mobile devices. Our evaluation of PolarTrack's tracking quality and comparison with state-of-the-art camera-based multi-device tracking showed a better...

Manifold Regularized Correlation Object Tracking.

Science.gov (United States)

Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

2018-05-01

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.

Multi-template Scale-Adaptive Kernelized Correlation Filters

KAUST Repository

Bibi, Adel Aamer

2015-12-07

This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

Multi-template Scale-Adaptive Kernelized Correlation Filters

KAUST Repository

Bibi, Adel Aamer; Ghanem, Bernard

2015-01-01

This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

Looping tracks associated with tropical cyclones approaching an isolated mountain. Part I: Essential parameters

Science.gov (United States)

Huang, Yi-Chih; Lin, Yuh-Lang

2018-06-01

Essential parameters for making a looping track when a westward-moving tropical cyclone (TC) approaches a mesoscale mountain are investigated by examining several key nondimensional control parameters with a series of systematic, idealized numerical experiments, such as U/ Nh, V max/ Nh, U/ fL x , V max/ fR, h/ L x , and R/ L y . Here U is the uniform zonal wind velocity, N the Brunt-Vaisala frequency, h the mountain height, f the Coriolis parameter, V max the maximum tangential velocity at a radius of R from the cyclone center and L x is the halfwidth of the mountain in the east-west direction. It is found that looping tracks (a) tend to occur under small U/ Nh and U/ fL x , moderate h/ L x , and large V max/ Nh, which correspond to slow movement (leading to subgeostrophic flow associated with strong orographic blocking), moderate steepness, and strong tangential wind associated with TC vortex; (b) are often accompanied by an area of perturbation high pressure to the northeast of the mountain, which lasts for only a short period; and (c) do not require the existence of a northerly jet. The nondimensional control parameters are consolidated into a TC looping index (LI), {U2 R2 }/{V_{max 2 hLy }} , which is tested by several historical looping and non-looping typhoons approaching Taiwan's Central Mountain Range (CMR) from east or southeast. It is found that LI < 0.0125 may serve as a criterion for looping track to occur.

Looping tracks associated with tropical cyclones approaching an isolated mountain. Part I: Essential parameters

Science.gov (United States)

Huang, Yi-Chih; Lin, Yuh-Lang

2017-05-01

Essential parameters for making a looping track when a westward-moving tropical cyclone (TC) approaches a mesoscale mountain are investigated by examining several key nondimensional control parameters with a series of systematic, idealized numerical experiments, such as U/Nh, V max/Nh, U/fL x , V max/fR, h/L x , and R/L y . Here U is the uniform zonal wind velocity, N the Brunt-Vaisala frequency, h the mountain height, f the Coriolis parameter, V max the maximum tangential velocity at a radius of R from the cyclone center and L x is the halfwidth of the mountain in the east-west direction. It is found that looping tracks (a) tend to occur under small U/Nh and U/fL x , moderate h/L x , and large V max/Nh, which correspond to slow movement (leading to subgeostrophic flow associated with strong orographic blocking), moderate steepness, and strong tangential wind associated with TC vortex; (b) are often accompanied by an area of perturbation high pressure to the northeast of the mountain, which lasts for only a short period; and (c) do not require the existence of a northerly jet. The nondimensional control parameters are consolidated into a TC looping index (LI), {U2 R2 }/{V_{max}2 hLy }} , which is tested by several historical looping and non-looping typhoons approaching Taiwan's Central Mountain Range (CMR) from east or southeast. It is found that LI < 0.0125 may serve as a criterion for looping track to occur.

RSSI based indoor tracking in sensor networks using Kalman filters

DEFF Research Database (Denmark)

Tøgersen, Frede Aakmann; Skjøth, Flemming; Munksgaard, Lene

2010-01-01

We propose an algorithm for estimating positions of devices in a sensor network using Kalman filtering techniques. The specific area of application is monitoring the movements of cows in a barn. The algorithm consists of two filters. The first filter enhances the signal-to-noise ratio...

Mixed labelling in multitarget particle filtering

NARCIS (Netherlands)

Boers, Y.; Sviestins, Egils; Driessen, Hans

2010-01-01

The so-called mixed labelling problem inherent to a joint state multitarget particle filter implementation is treated. The mixed labelling problem would be prohibitive for track extraction from a joint state multitarget particle filter. It is shown, using the theory of Markov chains, that the mixed

Optimization of band-pass filtering parameters of a Raman lidar detecting atmospheric water vapor

International Nuclear Information System (INIS)

Cao, Kai-Fa; Hu, Shun-Xing; Wang, Ying-jian

2012-01-01

It is very important for daytime Raman lidar measurement of water vapor to determine the parameters of a band-pass filter, which are pertinent to the lidar signal to noise ratio (SNR). The simulated annealing (SA) algorithm method has an advantage in finding the extremum of a certain cost function. In this paper, the Raman spectrum of water vapor is simulated and then a first realization of a simulated annealing algorithm in the optimization of a band-pass filter of a Raman lidar system designed to detect daytime water vapor is presented. The simulated results indicate that the narrow band-pass filter has higher SNR than the wide filter does but there would be an increase in the temperature sensitivity of a narrowband Raman water vapor lidar in the upper troposphere. The numerical simulation indicates that the magnitude of the temperature dependent effect can reach 3.5% or more for narrow band-pass Raman water vapor measurements so it is necessary to consider a new water vapor Raman lidar equation that permits the temperature sensitivity of these equations to be confined to a single term. (paper)

Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter

Science.gov (United States)

Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.

2014-07-01

The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.

Kalman Orbit Optimized Loop Tracking

Science.gov (United States)

Young, Lawrence E.; Meehan, Thomas K.

2011-01-01

Under certain conditions of low signal power and/or high noise, there is insufficient signal to noise ratio (SNR) to close tracking loops with individual signals on orbiting Global Navigation Satellite System (GNSS) receivers. In addition, the processing power available from flight computers is not great enough to implement a conventional ultra-tight coupling tracking loop. This work provides a method to track GNSS signals at very low SNR without the penalty of requiring very high processor throughput to calculate the loop parameters. The Kalman Orbit-Optimized Loop (KOOL) tracking approach constitutes a filter with a dynamic model and using the aggregate of information from all tracked GNSS signals to close the tracking loop for each signal. For applications where there is not a good dynamic model, such as very low orbits where atmospheric drag models may not be adequate to achieve the required accuracy, aiding from an IMU (inertial measurement unit) or other sensor will be added. The KOOL approach is based on research JPL has done to allow signal recovery from weak and scintillating signals observed during the use of GPS signals for limb sounding of the Earth s atmosphere. That approach uses the onboard PVT (position, velocity, time) solution to generate predictions for the range, range rate, and acceleration of the low-SNR signal. The low- SNR signal data are captured by a directed open loop. KOOL builds on the previous open loop tracking by including feedback and observable generation from the weak-signal channels so that the MSR receiver will continue to track and provide PVT, range, and Doppler data, even when all channels have low SNR.

Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

Science.gov (United States)

Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

1989-03-01

A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

Natural uranium impurities in fission track detectors and associated geocronological parameters

International Nuclear Information System (INIS)

Ricabarra, G.H.; Bovisio de Ricabarra, M.D.; Waisman, Dina; Faradjie de Turjanski, Rosa

1981-01-01

A technique, based in counting neutron induced fission tracks, has been developed for the measurement of uranium impurities in mica. Uranium concentrations of 10 -10 and 10 -9 (U atom/mica atom) have been measured. As a part of the development of this technique, the mica geological age was also measured, by fossil and induced track detection. The agreement obtained by this method, T = (472+-52) x 10 6 years with that of (450+-15) x 10 6 years obtained by the Ar-K technique is satisfactory and is an indirect test of the fission track technique used. A careful analysis of the neutron field parameters and nuclear data used in the age determination was made. This analysis is useful for applications in geocronology. According to this analysis a value of lambdasub(f)=(7.1+-0.1) x 10 -17 years -1 is recommended for the spontaneous fission of U238. However, in order to compare the results, the quoted age, T=(472+-52) x 10 6 years, was obtained with the generally accepted value of lambdasub(f)=(6.85-0.20) x 10 -17 years -1 (Fleischer and Price 1964). (author) [es

A spatial track formation model and its use for calculating etch-pit parameters of light nuclei

International Nuclear Information System (INIS)

Somogyi, G.; Scherzer, R.; Grabisch, K.; Enge, W.

1976-01-01

A generalized geometrical model of etch-pit formation in three dimensions is presented for nuclear particles entering isotropic solids at arbitrary angles of incidence. With this model one can calculate the relations between any particle parameter /Z = charge, M = mass, R = range, theta = angle of incidence/ and etching or track parameter /h = removed detector layer, L = track length, d = track diameter, etch-pit profile and contour/ for track etching rates varying monotonically along the trajectory of particles. Using a computer algorithm, calculations have been performed to study identification problems of nuclei of Z = 1-8 registered in a stack of polycarbonate sheets. For these calculations the etching rate ratio vs residual range curves were parametrized with a form of V -1 (R) = 1-Σasub(i) exp (- bsub(i)R) which does not involve the existence of a threshold for track registration. Particular attention was paid to the study of the evolution of etch-pit sizes for relatively high values of h. For this case, data are presented for the charge and isotope resolving power of the identification methods based on the relations L(R) of d(R). Calculations were also made to show the effect of the relative /parallel and opposite/ orientations between the directions of track etching and particle speed on etch-pit evolution. These studies offered new identification methods based on the determination of the curves L(parallel) vs L(opposite) and d(parallel) vs d(opposite), respectively. (orig.) [de

Occlusion Handling in Videos Object Tracking: A Survey

International Nuclear Information System (INIS)

Lee, B Y; Liew, L H; Cheah, W S; Wang, Y C

2014-01-01

Object tracking in video has been an active research since for decades. This interest is motivated by numerous applications, such as surveillance, human-computer interaction, and sports event monitoring. Many challenges related to tracking objects still remain, this can arise due to abrupt object motion, changing appearance patterns of objects and the scene, non-rigid object structures and most significant are occlusion of tracked object be it object-to-object or object-to-scene occlusions. Generally, occlusion in object tracking occur under three situations: self-occlusion, inter-object occlusion by background scene structure. Self-occlusion occurs most frequently while tracking articulated objects when one part of the object occludes another. Inter-object occlusion occurs when two objects being tracked occlude each other whereas occlusion by the background occurs when a structure in the background occludes the tracked objects. Typically, tracking methods handle occlusion by modelling the object motion using linear and non-linear dynamic models. The derived models will be used to continuously predicting the object location when a tracked object is occluded until the object reappears. Example of these method are Kalman filtering and Particle filtering trackers. Researchers have also utilised other features to resolved occlusion, for example, silhouette projections, colour histogram and optical flow. We will present some result from a previously conducted experiment when tracking single object using Kalman filter, Particle filter and Mean Shift trackers under various occlusion situation in this paper. We will also review various other occlusion handling methods that involved using multiple cameras. In a nutshell, the goal of this paper is to discuss in detail the problem of occlusion in object tracking and review the state of the art occlusion handling methods, classify them into different categories, and identify new trends. Moreover, we discuss the important

Occlusion Handling in Videos Object Tracking: A Survey

Science.gov (United States)

Lee, B. Y.; Liew, L. H.; Cheah, W. S.; Wang, Y. C.

2014-02-01

Object tracking in video has been an active research since for decades. This interest is motivated by numerous applications, such as surveillance, human-computer interaction, and sports event monitoring. Many challenges related to tracking objects still remain, this can arise due to abrupt object motion, changing appearance patterns of objects and the scene, non-rigid object structures and most significant are occlusion of tracked object be it object-to-object or object-to-scene occlusions. Generally, occlusion in object tracking occur under three situations: self-occlusion, inter-object occlusion by background scene structure. Self-occlusion occurs most frequently while tracking articulated objects when one part of the object occludes another. Inter-object occlusion occurs when two objects being tracked occlude each other whereas occlusion by the background occurs when a structure in the background occludes the tracked objects. Typically, tracking methods handle occlusion by modelling the object motion using linear and non-linear dynamic models. The derived models will be used to continuously predicting the object location when a tracked object is occluded until the object reappears. Example of these method are Kalman filtering and Particle filtering trackers. Researchers have also utilised other features to resolved occlusion, for example, silhouette projections, colour histogram and optical flow. We will present some result from a previously conducted experiment when tracking single object using Kalman filter, Particle filter and Mean Shift trackers under various occlusion situation in this paper. We will also review various other occlusion handling methods that involved using multiple cameras. In a nutshell, the goal of this paper is to discuss in detail the problem of occlusion in object tracking and review the state of the art occlusion handling methods, classify them into different categories, and identify new trends. Moreover, we discuss the important

Adaptive Colour Feature Identification in Image for Object Tracking

Directory of Open Access Journals (Sweden)

Feng Su

2012-01-01

Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

Directory of Open Access Journals (Sweden)

Fengjun Hu

2016-01-01

Full Text Available For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction.

Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

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Wenjie Lou

2016-02-01

Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

Track reconstruction in CMS high luminosity environment

CERN Document Server

AUTHOR|(CDS)2067159

2016-01-01

The CMS tracker is the largest silicon detector ever built, covering 200 square meters and providing an average of 14 high-precision measurements per track. Tracking is essential for the reconstruction of objects like jets, muons, electrons and tau leptons starting from the raw data from the silicon pixel and strip detectors. Track reconstruction is widely used also at trigger level as it improves objects tagging and resolution.The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compat...

Track reconstruction in CMS high luminosity environment

CERN Document Server

Goetzmann, Christophe

2014-01-01

The CMS tracker is the largest silicon detector ever built, covering 200 square meters and providing an average of 14 high-precision measurements per track. Tracking is essential for the reconstruction of objects like jets, muons, electrons and tau leptons starting from the raw data from the silicon pixel and strip detectors. Track reconstruction is widely used also at trigger level as it improves objects tagging and resolution.The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compat...

An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model

International Nuclear Information System (INIS)

Zhang, Xu; Wang, Yujie; Yang, Duo; Chen, Zonghai

2016-01-01

Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy. - Highlights: • A novel space state equation is built to describe the pack dynamic behavior. • The dual filters method is used to estimate the pack state-of-charge. • Battery inconsistency is considered to analyze the pack usage efficiency. • The accuracy of the proposed method is verified under different conditions.

COMPARATIVE EVALUATION OF FILTERS USED IN TRACKING AIR TARGETS

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Y. I. Strekalovskaya

2015-01-01

Full Text Available Using an imitation model for a flow of heterogeneous air targets the comparative assessment of the αβ, αβγ and the Kalman filters efficiency is evaluated. In the case of slightly maneuvering target the difference in filters’ efficiency is statistically insignificant; in the case of sharp maneuvering the Kalman filter is significantly more precise.

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

Science.gov (United States)

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

Analysis of a Kalman filter based method for on-line estimation of atmospheric dispersion parameters using radiation monitoring data

DEFF Research Database (Denmark)

Drews, Martin; Lauritzen, Bent; Madsen, Henrik

2005-01-01

A Kalman filter method is discussed for on-line estimation of radioactive release and atmospheric dispersion from a time series of off-site radiation monitoring data. The method is based on a state space approach, where a stochastic system equation describes the dynamics of the plume model...... parameters, and the observables are linked to the state variables through a static measurement equation. The method is analysed for three simple state space models using experimental data obtained at a nuclear research reactor. Compared to direct measurements of the atmospheric dispersion, the Kalman filter...... estimates are found to agree well with the measured parameters, provided that the radiation measurements are spread out in the cross-wind direction. For less optimal detector placement it proves difficult to distinguish variations in the source term and plume height; yet the Kalman filter yields consistent...

A 3D Image Filter for Parameter-Free Segmentation of Macromolecular Structures from Electron Tomograms

Science.gov (United States)

Ali, Rubbiya A.; Landsberg, Michael J.; Knauth, Emily; Morgan, Garry P.; Marsh, Brad J.; Hankamer, Ben

2012-01-01

3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters—the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms. PMID:22479430

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance.

Science.gov (United States)

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Measurement of Water Quality Parameters for Before and After Maintenance Service in Water Filter System

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Shaharudin Nuraida

2017-01-01

Full Text Available An adequate supply of safe drinking water is one of major ways to obtain healthy life. Water filter system is one way to improve the water quality. However, to maintain the performance of the system, it need to undergo the maintenance service. This study evaluate the requirement of maintenance service in water filter system. Water quality was measured before and after maintenance service. Parameters measured were pH, turbidity, residual chlorine, nitrate and heavy metals and these parameters were compared with National Drinking Water Quality Standards. Collection of data were involved three housing areas in Johor. The quality of drinking water from water filter system were analysed using pH meter, turbidity meter, DR6000 and Inductively Coupled Plasma-Mass Spectrometer. pH value was increased from 16.4% for before maintenance services to 30.7% for after maintenance service. Increment of removal percentage for turbidity, residual chlorine and nitrate after maintenance were 21.5, 13.6 and 26.7, respectively. This result shows that maintenance service enhance the performance of the system. However, less significant of maintenance service for enhance the removal of heavy metals which the increment of removal percentage in range 0.3 to 9.8. Only aluminium shows percentage removal for after maintenance with 92.8% lower compared to before maintenance service with 95.5%.

Characterization of the Edges and Contrasts in a digital image with the variation of the Parameters of the High-pass Filters used in the Estimation of Atmospheric Visibility

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Martha C. Guzmán-Zapata

2013-11-01

Full Text Available This paper considers the edges and contrasts obtained with high-pass filters used in the estimation of daytime atmospheric visibility from digital images, and the behavior of these edges and contrasts is characterized by varying the parameters of high-pass filters such as the Ideal, Gaussian, and Homomorphic-Gaussian. A synthetic image of regions with different contrasts is used to apply different filters, then, we define an index to measure the quality of the edges obtained in the filtered image and it is used to analyze the results. The results show that both, the filter selection and the selection of its parameters: affects the characteristics and quality of the detected edges in the filtered image, also determine the amount of noise that the filter added to the image (artifacts that were not present in the original image, and also establish if achieved, or not, the edge detection. The results also show that the edge quality index reaches maximum values at certain combinations of the filters parameters, which means that some combinations of parameters reduce situations distorting the edges and distorting atmospheric visibility measures based on the Fourier transform. So these parameters which provide maximum quality edges are established as suitable for use in visibility measurement.

Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.

Science.gov (United States)

Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia

2012-01-01

Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.

Robust H∞ Filtering for Uncertain Neutral Stochastic Systems with Markovian Jumping Parameters and Time Delay

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Yajun Li

2015-01-01

Full Text Available This paper deals with the robust H∞ filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribed H∞ performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.

Computer Vision Tracking Using Particle Filters for 3D Position Estimation

Science.gov (United States)

2014-03-27

List of Acronyms Acronym Definition AFIT Air Force Institute of Technology ASIR Auxiliary Sampling Importance Re-sampling BPF Bootstrap Particle Filter...Auxiliary Sampling Importance Re-sampling ( ASIR ) filter, and Regularized Particle Filter (RPF), also seek to eliminate weight collapse through a variety

Kalman Filtering with Real-Time Applications

CERN Document Server

Chui, Charles K

2009-01-01

Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.

Meta-optimization of the extended kalman filter's parameters for improved feature extraction on hyper-temporal images

CSIR Research Space (South Africa)

Salmon, BP

2011-07-01

Full Text Available . This paper proposes a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter to rapidly and efficiently estimate the features for the pair of triply modulated cosine functions. The approach is based on a unsupervised...

Hydrodynamics of microbial filter feeding

DEFF Research Database (Denmark)

Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia

2017-01-01

Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate......-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude......; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet...

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator.

Science.gov (United States)

Wang, Zhiqiang; Li, Xiaolong; Xie, Yunde; Long, Zhiqiang

2018-05-24

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator's frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator

Directory of Open Access Journals (Sweden)

Zhiqiang Wang

2018-05-01

Full Text Available In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator’s frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL. In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

Event-triggered cooperative target tracking in wireless sensor networks

Directory of Open Access Journals (Sweden)

Lu Kelin

2016-10-01

Full Text Available Since the issues of low communication bandwidth supply and limited battery capacity are very crucial for wireless sensor networks, this paper focuses on the problem of event-triggered cooperative target tracking based on set-membership information filtering. We study some fundamental properties of the set-membership information filter with multiple sensor measurements. First, a sufficient condition is derived for the set-membership information filter, under which the boundedness of the outer ellipsoidal approximation set of the estimation means is guaranteed. Second, the equivalence property between the parallel and sequential versions of the set-membership information filter is presented. Finally, the results are applied to a 1D event-triggered target tracking scenario in which the negative information is exploited in the sense that the measurements that do not satisfy the triggering conditions are modelled as set-membership measurements. The tracking performance of the proposed method is validated with extensive Monte Carlo simulations.

Kalman filter based data fusion for neutral axis tracking in wind turbine towers

Science.gov (United States)

Soman, Rohan; Malinowski, Pawel; Ostachowicz, Wieslaw; Paulsen, Uwe S.

2015-03-01

Wind energy is seen as one of the most promising solutions to man's ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.

Primer uticaja filtriranja slike u sistemima za praćenje ciljeva primenom termovizije / An example of image filtering in target tracking systems with thermal imagery

Directory of Open Access Journals (Sweden)

Zvonko M. Radosavljević

2003-07-01

Full Text Available U radu je dat primer primene jedne vrste niskofrekventnog filtriranja sa usrednjavanjem, koje se primenjuje u sistemima za detekciju i praćenje ciljeva u vazdušnom prostoru primenom termovizije. Date su dve metode filtriranja slike. Prva metoda koristi niskofrekventno konvoluciono filtriranje a druga usrednjavajući filtar na osnovu srednje vrednosti nivoa sivog. Ovi filtri su primenjeni u sistemima za praćenje uz pomoć infracrvenih senzora. Određivanje nivoa praga filtriranja vrši se uz pomoć statističkih osobina slike. Veoma važan korak u procesu praćenja je određivanje prozora praćenja, koji maze biti, po dimenzijama, fiksan ili adaptibilan. Pogrešna procena o postojanju cilja u prozoru može se doneti u slučaju prisustva šuma pozadine, predpojačavača, detektora, itd. Filtriranje je neophodan korak u ovim sistemima, kao značajan činilac U povećanju brzine i tačnosti praćenja. / A case of image filtering in air target detecting and tracking systems is described in this paper. Two image filtering methods are given. The first method is performed using a low pass convolving filter and the second one uses the mean value of gray level filter. The main goal of the cited filtering is implementation in IR (infra red systems. Some statistical features of the images were used for selecting the threshold level. The next step in the algorithm is the determination of a 'tracking window' that can be fixed or adaptive in size. A false estimation of a target existing in the window may be influenced by the background noise, low noise amplifier detector, etc.

Tracking Multiple Topics for Finding Interesting Articles

Energy Technology Data Exchange (ETDEWEB)

Pon, R K; Cardenas, A F; Buttler, D J; Critchlow, T J

2008-01-03

We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 25% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback.

Effects of statistical quality, sampling rate and temporal filtering techniques on the extraction of functional parameters from the left ventricular time-activity curves

Energy Technology Data Exchange (ETDEWEB)

Guignard, P.A.; Chan, W. (Royal Melbourne Hospital, Parkville (Australia). Dept. of Nuclear Medicine)

1984-09-01

Several techniques for the processing of a series of curves derived from two left ventricular time-activity curves acquired at rest and during exercise with a nuclear stethoscope were evaluated. They were three and five point time smoothing. Fourier filtering preserving one to four harmonics (H), truncated curve Fourier filtering, and third degree polynomial curve fitting. Each filter's ability to recover, with fidelity, systolic and diastolic function parameters was evaluated under increasingly 'noisy' conditions and at several sampling rates. Third degree polynomial curve fittings and truncated Fourier filters exhibited very high sensitivity to noise. Three and five point time smoothing had moderate sensitivity to noise, but were highly affected by sampling rate. Fourier filtering preserving 2H or 3H produced the best compromise with high resilience to noise and independence of sampling rate as far as the recovery of these functional parameters is concerned.

Effects of statistical quality, sampling rate and temporal filtering techniques on the extraction of functional parameters from the left ventricular time-activity curves

International Nuclear Information System (INIS)

Guignard, P.A.; Chan, W.

1984-01-01

Several techniques for the processing of a series of curves derived from two left ventricular time-activity curves acquired at rest and during exercise with a nuclear stethoscope were evaluated. They were three and five point time smoothing. Fourier filtering preserving one to four harmonics (H), truncated curve Fourier filtering, and third degree polynomial curve fitting. Each filter's ability to recover, with fidelity, systolic and diastolic function parameters was evaluated under increasingly 'noisy' conditions and at several sampling rates. Third degree polynomial curve fittings and truncated Fourier filters exhibited very high sensitivity to noise. Three and five point time smoothing had moderate sensitivity to noise, but were highly affected by sampling rate. Fourier filtering preserving 2H or 3H produced the best compromise with high resilience to noise and independence of sampling rate as far as the recovery of these functional parameters is concerned. (author)

Integrated tracking, classification, and sensor management theory and applications

CERN Document Server

Krishnamurthy, Vikram; Vo, Ba-Ngu

2012-01-01

A unique guide to the state of the art of tracking, classification, and sensor management. This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques.

The singular value filter: a general filter design strategy for PCA-based signal separation in medical ultrasound imaging.

Science.gov (United States)

Mauldin, F William; Lin, Dan; Hossack, John A

2011-11-01

A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.

A game theory approach to target tracking in sensor networks.

Science.gov (United States)

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

Urinary concentrations of benzophenone-type ultra violet light filters and reproductive parameters in young men

DEFF Research Database (Denmark)

Adoamnei, Evdochia; Mendiola, Jaime; Moñino-García, Miriam

2018-01-01

positively associated with T/E2 (β = 0.04, 95%CI: 0.002; 0.07) and negatively with inhibin b/FSH (β = -0.11, 95%CI: -0.21; -0.006) ratio. No significant associations were found between other urinary BP-type UV filters and other reproductive hormone levels or between any semen parameters and any...

Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters

Directory of Open Access Journals (Sweden)

Sicuranza Giovanni L

2004-01-01

Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.

Definition of parameters for quality assurance of flattening filter free (FFF) photon beams in radiation therapy

International Nuclear Information System (INIS)

Fogliata, A.; Garcia, R.; Knöös, T.; Nicolini, G.; Clivio, A.; Vanetti, E.; Khamphan, C.; Cozzi, L.

2012-01-01

Purpose: Flattening filter free (FFF) beams generated by medical linear accelerators have recently started to be used in radiotherapy clinical practice. Such beams present fundamental differences with respect to the standard filter flattened (FF) beams, making the generally used dosimetric parameters and definitions not always viable. The present study will propose possible definitions and suggestions for some dosimetric parameters for use in quality assurance of FFF beams generated by medical linacs in radiotherapy. Methods: The main characteristics of the photon beams have been analyzed using specific data generated by a Varian TrueBeam linac having both FFF and FF beams of 6 and 10 MV energy, respectively. Results: Definitions for dose profile parameters are suggested starting from the renormalization of the FFF with respect to the corresponding FF beam. From this point the flatness concept has been translated into one of “unflatness” and other definitions have been proposed, maintaining a strict parallelism between FFF and FF parameter concepts. Conclusions: Ideas for quality controls used in establishing a quality assurance program when introducing FFF beams into the clinical environment are given here, keeping them similar to those used for standard FF beams. By following the suggestions in this report, the authors foresee that the introduction of FFF beams into a clinical radiotherapy environment will be as safe and well controlled as standard beam modalities using the existing guidelines.

Personal Dosemeter of Thermal Neutron Using A Cr-39 Detector with Filter Natural LiF

International Nuclear Information System (INIS)

Sofyan, Hasnel; Thamrin, M.Thoyib

1996-01-01

The research of personal dosemeter for thermal neutron using Cr-39 detector with different thicknesses of natural LiF filter was carried out. The irradiation of Cr-39 detector with neutron source from reactor research TRIGA mark II of Rikkyo University Tokyo, Japan. Nuclear track was counted by automatic method with ASPECT ver.4.22 Series A4T124 software and manual method for correction. The result of research, the maximum of nuclear tracks was obtained at 8 mm of LiF filter was 10 mm with 11,630x10E-5 track/neutron for air radiation. And on phantom radiation, the thickness of filter was 10 mm with 11,630x10E-5 track/neutron. Its values were 3.6 and 7.5 bigger than the response of Cr-39 non filter in air and on phantom radiation, respectively

Kalman Filter Based Data Fusion for Bi-Axial Neutral Axis Tracking in Wind Turbine Towers

DEFF Research Database (Denmark)

Soman, Rohan; Malinowski, Pawel; Schmidt Paulsen, Uwe

2015-01-01

demonstrates a methodology for the selection of threshold for damage detection based on qualitative data acquired from several damage scenarios of a 10 MW wind turbine. The damage indicator is the change of neutral axis (NA) which is tracked using Kalman Filter (KF). Based on the level of damage to be detected...... in the structure is reflected by a change in this feature. If this change is above a threshold the structure is said to be damaged. In most applications the determination of this threshold is based on engineering judgment and the previous experience of the operator. These practices are highly subjective...... and the probability of occurrence of false positive and false negative detections, a threshold value is selected. This threshold is then applied to strain data from the Nordtank NTK500/41 wind turbine for validation....

Investigation of the influence of image reconstruction filter and scan parameters on operation of automatic tube current modulation systems for different CT scanners

International Nuclear Information System (INIS)

Sookpeng, Supawitoo; Martin, Colin J.; Gentle, David J.

2015-01-01

Variation in the user selected CT scanning parameters under automatic tube current modulation (ATCM) between hospitals has a substantial influence on the radiation doses and image quality for patients. The aim of this study was to investigate the effect of changing image reconstruction filter and scan parameter settings on tube current, dose and image quality for various CT scanners operating under ATCM. The scan parameters varied were pitch factor, rotation time, collimator configuration, kVp, image thickness and image filter convolution (FC) used for reconstruction. The Toshiba scanner varies the tube current to achieve a set target noise. Changes in the FC setting and image thickness for the first reconstruction were the major factors affecting patient dose. A two-step change in FC from smoother to sharper filters doubles the dose, but is counterbalanced by an improvement in spatial resolution. In contrast, Philips and Siemens scanners maintained tube current values similar to those for a reference image and patient, and the tube current only varied slightly for changes in individual CT scan parameters. The selection of a sharp filter increased the image noise, while use of iDose iterative reconstruction reduced the noise. Since the principles used by CT manufacturers for ATCM vary, it is important that parameters which affect patient dose and image quality for each scanner are made clear to operator to aid in optimisation. (authors)

Kaon Filtering For CLAS Data

International Nuclear Information System (INIS)

McNabb, J.

2001-01-01

The analysis of data from CLAS is a multi-step process. After the detectors for a given running period have been calibrated, the data is processed in the so called pass-1 cooking. During the pass-1 cooking each event is reconstructed by the program a1c which finds particle tracks and computes momenta from the raw data. The results are then passed on to several data monitoring and filtering utilities. In CLAS software, a filter is a parameterless function which returns an integer indicating whether an event should be kept by that filter or not. There is a main filter program called g1-filter which controls several specific filters and outputs several files, one for each filter. These files may then be analyzed separately, allowing individuals interested in one reaction channel to work from smaller files than using the whole data set would require. There are several constraints on what the filter functions should do. Obviously, the filtered files should be as small as possible, however the filter should also not reject any events that might be used in the later analysis for which the filter was intended

Generalized Selection Weighted Vector Filters

Directory of Open Access Journals (Sweden)

Rastislav Lukac

2004-09-01

Full Text Available This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03 in Grado, Italy.

Kalman filtering with real-time applications

CERN Document Server

Chui, Charles K

2017-01-01

This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...

Filtering Performance Comparison of Kernel and Wavelet Filters for Reactivity Signal Noise

International Nuclear Information System (INIS)

Park, Moon Ghu; Shin, Ho Cheol; Lee, Yong Kwan; You, Skin

2006-01-01

Nuclear reactor power deviation from the critical state is a parameter of specific interest defined by the reactivity measuring neutron population. Reactivity is an extremely important quantity used to define many of the reactor startup physics parameters. The time dependent reactivity is normally determined by solving the using inverse neutron kinetics equation. The reactivity computer is a device to provide an on-line solution of the inverse kinetics equation. The measurement signal of the neutron density is normally noise corrupted and the control rods movement typically gives reactivity variation with edge signals like saw teeth. Those edge regions should be precisely preserved since the measured signal is used to estimate the reactivity wroth which is a crucial parameter to assure the safety of the nuclear reactors. In this paper, three kind of edge preserving noise filters are proposed and their performance is demonstrated using stepwise signals. The tested filters are based on the unilateral, bilateral kernel and wavelet filters which are known to be effective in edge preservation. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

Science.gov (United States)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

A simulation to study the feasibility of improving the temporal resolution of LAGEOS geodynamic solutions by using a sequential process noise filter

Science.gov (United States)

Hartman, Brian Davis

1995-01-01

A key drawback to estimating geodetic and geodynamic parameters over time based on satellite laser ranging (SLR) observations is the inability to accurately model all the forces acting on the satellite. Errors associated with the observations and the measurement model can detract from the estimates as well. These 'model errors' corrupt the solutions obtained from the satellite orbit determination process. Dynamical models for satellite motion utilize known geophysical parameters to mathematically detail the forces acting on the satellite. However, these parameters, while estimated as constants, vary over time. These temporal variations must be accounted for in some fashion to maintain meaningful solutions. The primary goal of this study is to analyze the feasibility of using a sequential process noise filter for estimating geodynamic parameters over time from the Laser Geodynamics Satellite (LAGEOS) SLR data. This evaluation is achieved by first simulating a sequence of realistic LAGEOS laser ranging observations. These observations are generated using models with known temporal variations in several geodynamic parameters (along track drag and the J(sub 2), J(sub 3), J(sub 4), and J(sub 5) geopotential coefficients). A standard (non-stochastic) filter and a stochastic process noise filter are then utilized to estimate the model parameters from the simulated observations. The standard non-stochastic filter estimates these parameters as constants over consecutive fixed time intervals. Thus, the resulting solutions contain constant estimates of parameters that vary in time which limits the temporal resolution and accuracy of the solution. The stochastic process noise filter estimates these parameters as correlated process noise variables. As a result, the stochastic process noise filter has the potential to estimate the temporal variations more accurately since the constraint of estimating the parameters as constants is eliminated. A comparison of the temporal

Event-Based Variance-Constrained ${\\mathcal {H}}_{\\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements.

Science.gov (United States)

Wang, Licheng; Wang, Zidong; Han, Qing-Long; Wei, Guoliang

2018-03-01

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

Adaptive projective filters

International Nuclear Information System (INIS)

Dikusar, N.D.

1993-01-01

The new approach to solving of the finding problem is proposed. The method is based on Discrete Projective Transformations (DPT), the List Square Fitting (LSF) and uses the information feedback in tracing for linear or quadratic track segments (TS). The fast and stable with respect to measurement errors and background points recurrent algorithm is suggested. The algorithm realizes the family of digital adaptive projective filters (APF) with known nonlinear weight functions-projective invariants. APF can be used in adequate control systems for collection, processing and compression of data, including tracking problems for the wide class of detectors. 10 refs.; 9 figs

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

Science.gov (United States)

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-01-01

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. PMID:27618058

Tracking magma volume recovery at okmok volcano using GPS and an unscented kalman filter

Science.gov (United States)

Fournier, T.; Freymueller, Jeffrey T.; Cervelli, Peter

2009-01-01

Changes beneath a volcano can be observed through position changes in a GPS network, but distinguishing the source of site motion is not always straightforward. The records of continuous GPS sites provide a favorable data set for tracking magma migration. Dense campaign observations usually provide a better spatial picture of the overall deformation field, at the expense of an episodic temporal record. Combining these observations provides the best of both worlds. A Kalman filter provides a means for integrating discrete and continuous measurements and for interpreting subtle signals. The unscented Kalman filter (UKF) is a nonlinear method for time-dependent observations. We demonstrate the application of this technique to deformation data by applying it to GPS data collected at Okmok volcano. Seven years of GPS observations at Okmok are analyzed using a Mogi source model and the UKF. The deformation source at Okmok is relatively stable at 2.5 km depth below sea level, located beneath the center of the caldera, which means the surface deformation is caused by changes in the strength of the source. During the 7 years of GPS observations more than 0.5 m of uplift has occurred, a majority of that during the time period January 2003 to July 2004. The total volume recovery at Okmok since the last eruption in 1997 is ??60-80%. The UKF allows us to solve simultaneously for the time-dependence of the source strength and for the location without a priori information about the source. ?? 2009 by the American Geophysical Union.

The Development of a Microbial Challenge Test with Acholeplasma laidlawii To Rate Mycoplasma-Retentive Filters by Filter Manufacturers.

Science.gov (United States)

Folmsbee, Martha; Lentine, Kerry Roche; Wright, Christine; Haake, Gerhard; Mcburnie, Leesa; Ashtekar, Dilip; Beck, Brian; Hutchison, Nick; Okhio-Seaman, Laura; Potts, Barbara; Pawar, Vinayak; Windsor, Helena

2014-01-01

Mycoplasma are bacteria that can penetrate 0.2 and 0.22 μm rated sterilizing-grade filters and even some 0.1 μm rated filters. Primary applications for mycoplasma filtration include large scale mammalian and bacterial cell culture media and serum filtration. The Parenteral Drug Association recognized the absence of standard industry test parameters for testing and classifying 0.1 μm rated filters for mycoplasma clearance and formed a task force to formulate consensus test parameters. The task force established some test parameters by common agreement, based upon general industry practices, without the need for additional testing. However, the culture medium and incubation conditions, for generating test mycoplasma cells, varied from filter company to filter company and was recognized as a serious gap by the task force. Standardization of the culture medium and incubation conditions required collaborative testing in both commercial filter company laboratories and in an Independent laboratory (Table I). The use of consensus test parameters will facilitate the ultimate cross-industry goal of standardization of 0.1 μm filter claims for mycoplasma clearance. However, it is still important to recognize filter performance will depend on the actual conditions of use. Therefore end users should consider, using a risk-based approach, whether process-specific evaluation of filter performance may be warranted for their application. Mycoplasma are small bacteria that have the ability to penetrate sterilizing-grade filters. Filtration of large-scale mammalian and bacterial cell culture media is an example of an industry process where effective filtration of mycoplasma is required. The Parenteral Drug Association recognized the absence of industry standard test parameters for evaluating mycoplasma clearance filters by filter manufacturers and formed a task force to formulate such a consensus among manufacturers. The use of standardized test parameters by filter manufacturers

Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf

Directory of Open Access Journals (Sweden)

Yao Wenlong

2015-09-01

Full Text Available Aiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF was presented, and it was imported into the marine SSP propulsion motor control system. The strong tracking filter was used to update particles in the new algorithm and produce importance densities. As a result, the problems of particle degeneracy and sample impoverishment were ameliorated, the propulsion motor states and the rotor resistance were estimated simultaneously using strong track filter (STF, and the tracking ability of marine SSP propulsion motor control system was improved. Simulation result shown that the improved EPF algorithm was not only improving the prediction accuracy of the motor states and the rotor resistance, but also it can satisfy the requirement of navigation in harbor. It had the better accuracy than EPF algorithm.

A Signal Decomposition Method for Ultrasonic Guided Wave Generated from Debonding Combining Smoothed Pseudo Wigner-Ville Distribution and Vold–Kalman Filter Order Tracking

Directory of Open Access Journals (Sweden)

Junhua Wu

2017-01-01

Full Text Available Carbon fibre composites have a promising application future of the vehicle, due to its excellent physical properties. Debonding is a major defect of the material. Analyses of wave packets are critical for identification of the defect on ultrasonic nondestructive evaluation and testing. In order to isolate different components of ultrasonic guided waves (GWs, a signal decomposition algorithm combining Smoothed Pseudo Wigner-Ville distribution and Vold–Kalman filter order tracking is presented. In the algorithm, the time-frequency distribution of GW is first obtained by using Smoothed Pseudo Wigner-Ville distribution. The frequencies of different modes are computed based on summation of the time-frequency coefficients in the frequency direction. On the basis of these frequencies, isolation of different modes is done by Vold–Kalman filter order tracking. The results of the simulation signal and the experimental signal reveal that the presented algorithm succeeds in decomposing the multicomponent signal into monocomponents. Even though components overlap in corresponding Fourier spectrum, they can be isolated by using the presented algorithm. So the frequency resolution of the presented method is promising. Based on this, we can do research about defect identification, calculation of the defect size, and locating the position of the defect.

State and parameter estimation in nonlinear systems as an optimal tracking problem

International Nuclear Information System (INIS)

Creveling, Daniel R.; Gill, Philip E.; Abarbanel, Henry D.I.

2008-01-01

In verifying and validating models of nonlinear processes it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, we present a framework for connecting a data signal with a model in a way that minimizes the required coupling yet allows the estimation of unknown parameters in the model. The need to evaluate unknown parameters in models of nonlinear physical, biophysical, and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. Our approach builds on existing work that uses synchronization as a tool for parameter estimation. We address some of the critical issues in that work and provide a practical framework for finding an accurate solution. In particular, we show the equivalence of this problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation

Application of Fractional Fourier Transform to Moving Target Indication via Along-Track Interferometry

Directory of Open Access Journals (Sweden)

Chiu Shen

2005-01-01

Full Text Available A relatively unknown yet powerful technique, the so-called fractional Fourier transform (FrFT, is applied to SAR along-track interferometry (SAR-ATI in order to estimate moving target parameters. By mapping a target's signal onto a fractional Fourier axis, the FrFT permits a constant-velocity target to be focused in the fractional Fourier domain thereby affording orders of magnitude improvement in SCR. Moving target velocity and position parameters are derived and expressed in terms of an optimum fractional angle and a measured fractional Fourier position , allowing a target to be accurately repositioned and its velocity components computed without actually forming an SAR image. The new estimation algorithm is compared with the matched filter bank approach, showing some of the advantages of the FrFT method. The proposed technique is applied to the data acquired by the two-aperture CV580 airborne radar system configured in its along-track mode. Results show that the method is effective in estimating target velocity and position parameters.

[Study on method of tracking the active cells in image sequences based on EKF-PF].

Science.gov (United States)

Tang, Chunming; Liu, Ying

2013-02-01

In cell image sequences, due to the nonlinear and nonGaussian motion characteristics of active cells, the accurate prediction and tracking is still an unsolved problem. We applied extended Kalman particle filter (EKF-PF) here in our study, attempting to solve the problem. Firstly we confirmed the existence and positions of the active cells. Then we established a motion model and improved it via adding motion angle estimation. Next we predicted motion parameters, such as displacement, velocity, accelerated velocity and motion angle, in region centers of the cells being tracked. Finally we obtained the motion traces of active cells. There were fourteen active cells in three image sequences which have been tracked. The errors were less than 2.5 pixels when the prediction values were compared with actual values. It showed that the presented algorithm may basically reach the solution of accurate predition and tracking of the active cells.

High resolution vertical profiles of wind, temperature and humidity obtained by computer processing and digital filtering of radiosonde and radar tracking data from the ITCZ experiment of 1977

Science.gov (United States)

Danielson, E. F.; Hipskind, R. S.; Gaines, S. E.

1980-01-01

Results are presented from computer processing and digital filtering of radiosonde and radar tracking data obtained during the ITCZ experiment when coordinated measurements were taken daily over a 16 day period across the Panama Canal Zone. The temperature relative humidity and wind velocity profiles are discussed.

Optimizing experimental parameters for tracking of diffusing particles

DEFF Research Database (Denmark)

Vestergaard, Christian L.

2016-01-01

We describe how a single-particle tracking experiment should be designed in order for its recorded trajectories to contain the most information about a tracked particle's diffusion coefficient. The precision of estimators for the diffusion coefficient is affected by motion blur, limited photon st...

Improved Kalman Filter-Based Speech Enhancement with Perceptual Post-Filtering

Institute of Scientific and Technical Information of China (English)

WEIJianqiang; DULimin; YANZhaoli; ZENGHui

2004-01-01

In this paper, a Kalman filter-based speech enhancement algorithm with some improvements of previous work is presented. A new technique based on spectral subtraction is used for separation speech and noise characteristics from noisy speech and for the computation of speech and noise Autoregressive (AR) parameters. In order to obtain a Kalman filter output with high audible quality, a perceptual post-filter is placed at the output of the Kalman filter to smooth the enhanced speech spectra.Extensive experiments indicate that this newly proposed method works well.

Study of the effects of atmospheric parameters on ground radon concentration by track technique

International Nuclear Information System (INIS)

Tidjani, Adams

1988-01-01

Radon emanation was continuously monitored for 24 months, accompanied by measurements of atmospheric parameters. Integrated measurments of radon concentrations have been performed with LR-115 cellulose nitrate track detectors. The monitoring was conducted at 16 sites distributed around the Dakar University area. Observed changes in radon concentration are interpreted as being caused by changes in meteorological conditions and ocean tides. (author)

Learning Rotation for Kernel Correlation Filter

KAUST Repository

Hamdi, Abdullah

2017-08-11

Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.

An efficient incremental learning mechanism for tracking concept drift in spam filtering.

Directory of Open Access Journals (Sweden)

Jyh-Jian Sheu

Full Text Available This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email's header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1 Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email's content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2 Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3 We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment.

Tracking of nuclear reactor parameters via recursive non linear estimation

International Nuclear Information System (INIS)

Pages Fita, J.; Alengrin, G.; Aguilar Martin, J.; Zwingelstein, M.

1975-01-01

The usefulness of nonlinear estimation in the supervision of nuclear reactors, as well for reactivity determination as for on-line modelisation in order to detect eventual and unwanted changes in working operation is illustrated. It is dealt with the reactivity estimation using an a priori dynamical model under the hypothesis of one group of delayed neutrons (measurements were done with an ionisation chamber). The determination of the reactivity using such measurements appears as a nonlinear estimation procedure derived from a particular form of nonlinear filter. Observed inputs being demand of power and inside temperature, and output being the reactivity balance, a recursive algorithm is derived for the estimation of the parameters that define the actual behavior of the reactor. Example of treatment of real data is given [fr

Factors Influencing HEPA Filter Performance

International Nuclear Information System (INIS)

Parsons, M.S.; Waggoner, Ch.A.

2009-01-01

Properly functioning HEPA air filtration systems depend on a variety of factors that start with the use of fully characterized challenge conditions for system design and then process control during operation. This paper addresses factors that should be considered during the design phase as well as operating parameters that can be monitored to ensure filter function and lifetime. HEPA filters used in nuclear applications are expected to meet design, fabrication, and performance requirements set forth in the ASME AG-1 standard. The DOE publication Nuclear Air Cleaning Handbook (NACH) is an additional guidance document for design and operation HEPA filter systems in DOE facilities. These two guidelines establish basic maximum operating parameters for temperature, maximum aerosol particle size, maximum particulate matter mass concentration, acceptable differential pressure range, and filter media velocity. Each of these parameters is discussed along with data linking variability of each parameter with filter function and lifetime. Temporal uncertainty associated with gas composition, temperature, and absolute pressure of the air flow can have a direct impact on the volumetric flow rate of the system with a corresponding impact on filter media velocity. Correlations between standard units of flow rate (standard meters per minute or cubic feet per minute) versus actual units of volumetric flow rate are shown for variations in relative humidity for a 70 deg. C to 200 deg. C temperature range as an example of gas composition that, uncorrected, will influence media velocity. The AG-1 standard establishes a 2.5 cm/s (5 feet per minute) ceiling for media velocities of nuclear grade HEPA filters. Data are presented that show the impact of media velocities from 2.0 to 4.0 cm/s media velocities (4 to 8 fpm) on differential pressure, filter efficiency, and filter lifetime. Data will also be presented correlating media velocity effects with two different particle size

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

KAUST Repository

El Gharamti, Mohamad

2015-11-26

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter.

Science.gov (United States)

Kim, Seongwan; Ban, Yuseok; Lee, Sangyoun

2017-01-17

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor's stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.

High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner.

Science.gov (United States)

Feng, Zhao; Ling, Jie; Ming, Min; Xiao, Xiao-Hui

2017-08-01

For precision motion, high-bandwidth and flexible tracking are the two important issues for significant performance improvement. Iterative learning control (ILC) is an effective feedforward control method only for systems that operate strictly repetitively. Although projection ILC can track varying references, the performance is still limited by the fixed-bandwidth Q-filter, especially for triangular waves tracking commonly used in a piezo nanopositioner. In this paper, a wavelet transform-based linear time-varying (LTV) Q-filter design for projection ILC is proposed to compensate high-frequency errors and improve the ability to tracking varying references simultaneously. The LVT Q-filter is designed based on the modulus maximum of wavelet detail coefficients calculated by wavelet transform to determine the high-frequency locations of each iteration with the advantages of avoiding cross-terms and segmenting manually. The proposed approach was verified on a piezo nanopositioner. Experimental results indicate that the proposed approach can locate the high-frequency regions accurately and achieve the best performance under varying references compared with traditional frequency-domain and projection ILC with a fixed-bandwidth Q-filter, which validates that through implementing the LTV filter on projection ILC, high-bandwidth and flexible tracking can be achieved simultaneously by the proposed approach.

An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.

Science.gov (United States)

Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

2016-01-01

This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.

Evidence for and implications of self-background of radon dosimeters with glass-fiber filters

International Nuclear Information System (INIS)

Put, L.W.; Lembrechts, J.; Graaf, E.R. van der; Stoop, P.

2000-01-01

The first national radon survey in the Netherlands was conducted in 1984 with passive radon dosimeters that contain glass-fiber diffusion filters. During the last few years, measurements of outdoor-radon concentrations and information in the literature suggested that these dosimeters may give falsely elevated readings. A systematic contribution would be present due to alpha particles from natural radionuclides in the glass-fiber filter producing tracks on the track-etch foil. In the framework of the quality assurance of their laboratories, the origin of this offset was systematically assessed by means of measurements of alpha and gamma radiation from the glass-fiber filters and by intercomparisons between different types of detectors at low radon concentrations. It was found that alpha particles from the decay of 214 Po in the glass-fiber filter are the main cause of the extra tracks (only 12% originates from decay of 212 Po), leading, for this type of filter, to an offset in concentration of approximately 8 Bq m -3 . The implications of this offset are discussed

Optimality based repetitive controller design for track-following servo system of optical disk drives.

Science.gov (United States)

Chen, Wentao; Zhang, Weidong

2009-10-01

In an optical disk drive servo system, to attenuate the external periodic disturbances induced by inevitable disk eccentricity, repetitive control has been used successfully. The performance of a repetitive controller greatly depends on the bandwidth of the low-pass filter included in the repetitive controller. However, owing to the plant uncertainty and system stability, it is difficult to maximize the bandwidth of the low-pass filter. In this paper, we propose an optimality based repetitive controller design method for the track-following servo system with norm-bounded uncertainties. By embedding a lead compensator in the repetitive controller, both the system gain at periodic signal's harmonics and the bandwidth of the low-pass filter are greatly increased. The optimal values of the repetitive controller's parameters are obtained by solving two optimization problems. Simulation and experimental results are provided to illustrate the effectiveness of the proposed method.

TUNING PARAMETER LINEAR QUADRATIC TRACKING MENGGUNAKAN ALGORITMA GENETIKA UNTUK PENGENDALIAN GERAK LATERAL QUADCOPTER

Directory of Open Access Journals (Sweden)

Farid Choirul Akbar

2016-04-01

Full Text Available Gerakan lateral quadcopter dapat dilakukan apabila quadcopter dapat menjaga kestabilan pada saat hover, sehingga quadcopter dapat melakukan gerak rotasi. Perubahan sudut roll akan mengakibatkan gerak translasi pada sumbu Y, sedangkan perubahan sudut pitch akan mengakibatkan gerak translasi pada sumbu X. Disisi lain, quadcopter merupakan suatu sistem non-linear dan memiliki kestabilan yang rendah sehingga rentan terhadap gangguan. Pada penelitian Tugas Akhir ini dirancang pengendalian gerak rotasi quadcopter menggunakan Linear Quadratic Regulator (LQR dan Linear Quadratic Tracking (LQT untuk pengendalian gerak translasi. Untuk mendapatkan parameter dari LQT digunakan Algoritma Genetika (GA. Hasil tuning GA yang digunakan pada LQT memiliki nilai Qx 700,1884, nilai Qy 700,6315, nilai Rx 0,1568, dan  nilai Ry 0,1579. Respon LQT tersebut memiliki RMSE pada sumbu X dan sumbu Y sebesar 1,99 % serta memiliki time lagging 0,35 detik. Dengan hasil tersebut quadcopter mampu men-tracking trajectory berbentuk segitigaTekni

Major parameters affecting the calculation of equilibrium factor using SSNTD-measured track densities

International Nuclear Information System (INIS)

Abo-Elmagd, M.; Mansy, M.; Eissa, H.M.; El-Fiki, M.A.

2006-01-01

The equilibrium factor F between radon and its daughters as a function of the track density ratio D/D 0 between bare and in can track detectors is solved graphically and gave more accurate solution than that solved mathematically elsewhere. The advantages of the graphical solution come from its simplicity and does not need any tedious mathematical formula or a computer program. The simplicity of this solution makes us study many parameters that affect the equilibrium factor determination such as the detector type, the diffusion chamber dimensions, the membrane specifications and the behavior of α-emitters around the detector. The results show that the equilibrium factor as a function of D/D 0 takes different form according to the facility used. The range of this study covers two widely used detectors (CR-39 and LR-115) equipped in two widely used diffusion chambers (small and medium chambers)

Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.

Science.gov (United States)

Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid

2015-11-07

Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2). Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

International Nuclear Information System (INIS)

Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

2009-01-01

Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

ATMS software: Fuzzy Hough Transform in a hybrid algorithm for counting the overlapped etched tracks and orientation recognition

International Nuclear Information System (INIS)

Khayat, O.; Ghergherehchi, M.; Afarideh, H.; Durrani, S.A.; Pouyan, Ali A.; Kim, Y.S.

2013-01-01

A computer program named ATMS written in MATLAB and running with a friendly interface has been developed for recognition and parametric measurements of etched tracks in images captured from the surface of Solid State Nuclear Track Detectors. The program, using image analysis tools, counts the number of etched tracks and depending on the current working mode classifies them according to their radii (small object removal) or their axis (non-perpendicular or non-circular etched tracks), their mean intensity value and their orientation through the minor and major axes. Images of the detectors' surfaces are input to the code, which generates text and figure files as output, including the number of counted etched tracks with the associated track parameters, histograms and a figure showing edge and center of detected etched tracks. ATMS code is running hierarchically as calibration, testing and measurement modes to demonstrate the reliability, repeatability and adaptability. Fuzzy Hough Transform is used for the estimation of the number of etched tracks and their parameters, providing results even in cases that overlapping and orientation occur. ATMS code is finally converted to a standalone file which makes it able to run out of MATLAB environment. - Highlights: ► Presenting a novel code named ATMS for nuclear track measurements. ► Execution in three modes for generality, adaptability and reliability. ► Using Fuzzy Hough Transform for overlapping detection and orientation recognition. ► Using DFT as a filter for noise removal process in track images. ► Processing the noisy track images and demonstration of the presented code

Tracking Subpixel Targets with Critically Sampled Optical Sensors

Science.gov (United States)

2012-09-01

LEFT BLANK xii LIST OF ACRONYMS AND ABBREVIATIONS PSF point spread function SNR signal-to-noise ratio SLAM simultaneous localization and tracking EO... LIDAR light detection and ranging FOV field of view RMS root mean squared PF particle filter TBD track before detect MCMC monte carlo markov chain

An adaptive filtering method based on EMD for X-ray pulsar navigation with uncertain measurement noise

Directory of Open Access Journals (Sweden)

Li N.

2017-01-01

Full Text Available Affected by the unstable pulse radiation and the pulsar directional errors, the statistical characteristics of the pulsar measurement noise may vary with time slowly and cannot be accurately determined, which cause the filtering accuracy of the extended Kalman filter(EKF in pulsar navigation positioning system decline sharply or even diverge. To solve this problem, an adaptive extended Kalman filtering algorithm based on the empirical mode decomposition(EMD is proposed. In this method, the high frequency noise is separated from measurement information of pulsar by the method of EMD, and the noise variance can be estimated to update the parameters of EKF. The simulation results demonstrate that compared with conventional EKF, the proposed method can adaptively track the change of the measurement noise, and still keeps high estimation accuracy with unknown measurement noise, the positioning accuracy of the pulsar navigation is improved simultaneously.

Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles

Directory of Open Access Journals (Sweden)

Lie Guo

2016-06-01

Full Text Available Pedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize a Kalman filter to predict the possible position of the pedestrian centroid in the future frame. A Camshift tracking algorithm is used to track the pedestrian in the specific search window of the next frame based on the prediction results. The actual position of the pedestrian centroid is output from the Camshift tracking algorithm to update the gain and error covariance matrix of the Kalman filter. Experimental results in real traffic situations show the proposed pedestrian tracking algorithm can achieve good performance even when they are partly occluded in inconsistent illumination circumstances.

Open-loop position tracking control of a piezoceramic flexible beam using a dynamic hysteresis compensator

International Nuclear Information System (INIS)

Nguyen, Phuong-Bac; Choi, Seung-Bok

2010-01-01

This paper proposes a novel hysteresis compensator to enhance control accuracy in open-loop position tracking control of a piezoceramic flexible beam. The proposed hysteresis compensator consists of two components: a rate-independent hysteresis compensator and a nonlinear filter. The compensator is formulated based on the inverse Preisach model, while the weight coefficients of the filter are identified adaptively using a recursive least square (RLS) algorithm. In this work, two dynamic hysteresis compensators (or rate-independent hysteresis compensators) are developed by adopting two different nonlinear filters: Volterra and bilinear filters. In order to demonstrate the improved control accuracy of the proposed dynamic compensators, a flexible beam associated with the piezoceramic actuator is modeled using the finite element method (FEM) and Euler–Bernoulli beam theory. The beam model is then integrated with the proposed hysteresis model to achieve accurate position tracking control at the tip of the beam. An experimental investigation on the tip position tracking control is undertaken by realizing three different hysteresis compensators: a rate-independent hysteresis compensator, a rate-dependent hysteresis compensator with a Volterra nonlinear filter and a rate-independent hysteresis compensator with a bilinear nonlinear filter. It is shown that the proposed dynamic hysteresis compensators can provide much better tracking control accuracy than conventional rate-independent hysteresis compensators

Cálculo de parámetros de filtros pasivos de armónicos; Calculation of the harmonics passive filters parameters

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Ignacio Pérez Abril

2012-07-01

Full Text Available Los filtros de armónicos cumplen la función de evitar la circulación de las corrientes de armónico por el sistema y reducir la distorsión de la tensión. Estos pueden ser pasivos (compuestos por arreglos de impedancias o activos(basados en electrónica de potencia. Las características de los filtros pasivos pueden encontrarse en la bibliografía especializada. Sin embargo, las ecuaciones para el diseño de los mismos no se muestran en todos los casos, lo que dificulta el cálculo de sus componentes y de su estrés en condiciones de operación. El objetivo fundamental de este trabajo es desarrollar el procedimiento general para el cálculo de los filtros pasivos de armónicos y determinar las ecuaciones correspondientes a los distintos tipos de filtro. Además, se describe una aplicación en Matlab que calcula los parámetros R, L y C de los distintos tipos de filtro y evalúa el estrés a que se someten los componentes de los mismos. The purpose of the harmonic filters in the electrical power systems is the avoiding the harmonic currents circulation in the network and the reduction of the voltages waveform distortion. The harmonic filters can be of passive type (composite of impedances or active type (based on power electronic. The characteristics of passive filters can be found in the specialized bibliography. However, the equations for the filter design are not showed in all cases, which difficult the filter’s components calculation and the evaluation of its stress in operation conditions. The objective of thepresented work is the developing of a general procedure for the harmonic passive filters parameters calculation and the determination of the needed equations for each type of filter. Besides, a Matlab application that calculates the R, L and C parameters, and the stress of all the treated filters is showed.

Research on the filtering algorithm in speed and position detection of maglev trains.

Science.gov (United States)

Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

2011-01-01

This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train's structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.

Kalman Filter Application to Symmetrical Fault Detection during Power Swing

DEFF Research Database (Denmark)

Khodaparast, Jalal; Silva, Filipe Miguel Faria da; Khederzadeh, M.

2016-01-01

capability of Kalman Filter. The proposed index is calculated by assessing the difference between predicted and actual samples of impedance. The predicted impedance samples are obtained using Kalman filter and Taylor expansion, which is used in this paper to track the phasor precisely. Second order of Taylor...... expansion is used to decrease corrugation effect of impedance estimation and increase the reliability of proposed method. The instantaneous estimation and prediction capability of Kalman filter are two reasons for proposing utilizing Kalman filter....

Real time eye tracking using Kalman extended spatio-temporal context learning

Science.gov (United States)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter

Energy Technology Data Exchange (ETDEWEB)

Bajla, Ivan [Austrian Research Centres Sibersdorf, Department of High Performance Image Processing and Video-Technology, A-2444 Seibersdorf (Austria); Hollander, Igor [Institute of information Processing, Austrian Academy of Sciences, Sonnenfelsgasse 19/2, 1010 Wien (Austria)

1999-12-31

A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors) 6 refs., 3 figs.2 tabs.

Geometry-Driven-Diffusion filtering of MR Brain Images using dissimilarities and optimal relaxation parameter

International Nuclear Information System (INIS)

Bajla, Ivan; Hollander, Igor

1998-01-01

A novel method of local adapting of the conductance using a pixel dissimilarity measure is developed. An alternative processing methodology is proposed, which is based on intensity gradient histogram calculated for region interiors and boundaries of a phantom which models real MR brain scans. It involves a specific cost function suitable for the calculation of the optimum relaxation parameter Kopt and for the selection of the optimal exponential conductance. Computer experiments for locally adaptive geometry-driven-diffusion filtering of an MR brain phantom have been performed and evaluated. (authors)

CMS reconstruction improvements for the tracking in large pileup events

CERN Document Server

Rovere, M

2015-01-01

The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compatible with the increasing instantaneous luminosity of LHC, resulting in a large number of primary vertices and tracks per bunch crossing.The major upgrade put in place during the present LHC Long Shutdown will allow the tracking code to comply with the conditions expected during RunII and the much larger pileup. In particular, new algorithms that are intrinsically more robust in high occupancy conditions were developed, iteration...

Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter

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Yasser Diab

2017-07-01

Full Text Available A real-time determination of battery parameters is challenging because batteries are non-linear, time-varying systems. The transient behaviour of lithium-ion batteries is modelled by a Thevenin-equivalent circuit with two time constants characterising activation and concentration polarization. An experimental approach is proposed for directly determining battery parameters as a function of physical quantities. The model’s parameters are a function of the state of charge and of the discharge rate. These can be expressed by regression equations in the model to derive a continuous-discrete extended Kalman estimator of the state of charge and of other parameters. This technique is based on numerical integration of the ordinary differential equations to predict the state of the stochastic dynamic system and the corresponding error covariance matrix. Then a standard correction step of the extended Kalman filter (EKF is applied to increase the accuracy of estimated parameters. Simulations resulting from this proposed estimator model were compared with experimental results under a variety of operating scenarios—analysis of the results demonstrate the accuracy of the estimator for correctly identifying battery parameters.

Box-Particle Cardinality Balanced Multi-Target Multi-Bernoulli Filter

OpenAIRE

L. Song; X. Zhao

2014-01-01

As a generalized particle filtering, the box-particle filter (Box-PF) has a potential to process the measurements affected by bounded error of unknown distributions and biases. Inspired by the Box-PF, a novel implementation for multi-target tracking, called box-particle cardinality balanced multi-target multi-Bernoulli (Box-CBMeMBer) filter is presented in this paper. More important, to eliminate the negative effect of clutters in the estimation of the numbers of targets, an improved generali...

Active power filter for harmonic compensation using a digital dual-mode-structure repetitive control approach

DEFF Research Database (Denmark)

Zou, Zhixiang; Wang, Zheng; Cheng, Ming

2012-01-01

This paper presents an digital dual-mode-structure repetitive control approach for the single-phase shunt active power filter (APF), which aims to enhance the tracking ability and eliminate arbitrary order harmonic. The proposed repetitive control scheme blends the characteristics of both odd......-harmonic repetitive control and even-harmonic repetitive control. Moreover, the convergence rate is faster than conventional repetitive controller. Additionally, the parameters have been designed and optimized for the dual-mode structure repetitive control to improve the performance of APF system. Experimental...

An auxiliary frequency tracking system for general purpose lock-in amplifiers

Science.gov (United States)

Xie, Kai; Chen, Liuhao; Huang, Anfeng; Zhao, Kai; Zhang, Hanlu

2018-04-01

Lock-in amplifiers (LIAs) are designed to measure weak signals submerged by noise. This is achieved with a signal modulator to avoid low-frequency noise and a narrow-band filter to suppress out-of-band noise. In asynchronous measurement, even a slight frequency deviation between the modulator and the reference may lead to measurement error because the filter’s passband is not flat. Because many commercial LIAs are unable to track frequency deviations, in this paper we propose an auxiliary frequency tracking system. We analyze the measurement error caused by the frequency deviation and propose both a tracking method and an auto-tracking system. This approach requires only three basic parameters, which can be obtained from any general purpose LIA via its communications interface, to calculate the frequency deviation from the phase difference. The proposed auxiliary tracking system is designed as a peripheral connected to the LIA’s serial port, removing the need for an additional power supply. The test results verified the effectiveness of the proposed system; the modified commercial LIA (model SR-850) was able to track the frequency deviation and continuous drift. For step frequency deviations, a steady tracking error of less than 0.001% was achieved within three adjustments, and the worst tracking accuracy was still better than 0.1% for a continuous frequency drift. The tracking system can be used to expand the application scope of commercial LIAs, especially for remote measurements in which the modulation clock and the local reference are separated.

The DOe Silicon Track Trigger

International Nuclear Information System (INIS)

Steinbrueck, Georg

2003-01-01

We describe a trigger preprocessor to be used by the DOe experiment for selecting events with tracks from the decay of long-lived particles. This Level 2 impact parameter trigger utilizes information from the Silicon Microstrip Tracker to reconstruct tracks with improved spatial and momentum resolutions compared to those obtained by the Level 1 tracking trigger. It is constructed of VME boards with much of the logic existing in programmable processors. A common motherboard provides the I/O infrastructure and three different daughter boards perform the tasks of identifying the roads from the tracking trigger data, finding the clusters in the roads in the silicon detector, and fitting tracks to the clusters. This approach provides flexibility for the design, testing and maintenance phases of the project. The track parameters are provided to the trigger framework in 25 μs. The effective impact parameter resolution for high-momentum tracks is 35 μm, dominated by the size of the Tevatron beam

The stochastic filtering problem: a brief historical account

OpenAIRE

Crisan, Dan

2014-01-01

Onwards from the mid-twentieth century, the stochastic filtering problem has caught the attention of thousands of mathematicians, engineers, statisticians, and computer scientists. Its applications span the whole spectrum of human endeavour, including satellite tracking, credit risk estimation, human genome analysis, and speech recognition. Stochastic filtering has engendered a surprising number of mathematical techniques for its treatment and has played an important role in...

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter

Directory of Open Access Journals (Sweden)

Seongwan Kim

2017-01-01

Full Text Available The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor, usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor’s stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.

Tracking of ball and players in beach volleyball videos.

Directory of Open Access Journals (Sweden)

Gabriel Gomez

Full Text Available This paper presents methods for the determination of players' positions and contact time points by tracking the players and the ball in beach volleyball videos. Two player tracking methods are compared, a classical particle filter and a rigid grid integral histogram tracker. Due to mutual occlusion of the players and the camera perspective, results are best for the front players, with 74,6% and 82,6% of correctly tracked frames for the particle method and the integral histogram method, respectively. Results suggest an improved robustness against player confusion between different particle sets when tracking with a rigid grid approach. Faster processing and less player confusions make this method superior to the classical particle filter. Two different ball tracking methods are used that detect ball candidates from movement difference images using a background subtraction algorithm. Ball trajectories are estimated and interpolated from parabolic flight equations. The tracking accuracy of the ball is 54,2% for the trajectory growth method and 42,1% for the Hough line detection method. Tracking results of over 90% from the literature could not be confirmed. Ball contact frames were estimated from parabolic trajectory intersection, resulting in 48,9% of correctly estimated ball contact points.

Tracking of Ball and Players in Beach Volleyball Videos

Science.gov (United States)

Gomez, Gabriel; Herrera López, Patricia; Link, Daniel; Eskofier, Bjoern

2014-01-01

This paper presents methods for the determination of players' positions and contact time points by tracking the players and the ball in beach volleyball videos. Two player tracking methods are compared, a classical particle filter and a rigid grid integral histogram tracker. Due to mutual occlusion of the players and the camera perspective, results are best for the front players, with 74,6% and 82,6% of correctly tracked frames for the particle method and the integral histogram method, respectively. Results suggest an improved robustness against player confusion between different particle sets when tracking with a rigid grid approach. Faster processing and less player confusions make this method superior to the classical particle filter. Two different ball tracking methods are used that detect ball candidates from movement difference images using a background subtraction algorithm. Ball trajectories are estimated and interpolated from parabolic flight equations. The tracking accuracy of the ball is 54,2% for the trajectory growth method and 42,1% for the Hough line detection method. Tracking results of over 90% from the literature could not be confirmed. Ball contact frames were estimated from parabolic trajectory intersection, resulting in 48,9% of correctly estimated ball contact points. PMID:25426936

Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains

Directory of Open Access Journals (Sweden)

Chunhui Dai

2011-07-01

Full Text Available This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.

A fast ellipse extended target PHD filter using box-particle implementation

Science.gov (United States)

Zhang, Yongquan; Ji, Hongbing; Hu, Qi

2018-01-01

This paper presents a box-particle implementation of the ellipse extended target probability hypothesis density (ET-PHD) filter, called the ellipse extended target box particle PHD (EET-BP-PHD) filter, where the extended targets are described as a Poisson model developed by Gilholm et al. and the term "box" is here equivalent to the term "interval" used in interval analysis. The proposed EET-BP-PHD filter is capable of dynamically tracking multiple ellipse extended targets and estimating the target states and the number of targets, in the presence of clutter measurements, false alarms and missed detections. To derive the PHD recursion of the EET-BP-PHD filter, a suitable measurement likelihood is defined for a given partitioning cell, and the main implementation steps are presented along with the necessary box approximations and manipulations. The limitations and capabilities of the proposed EET-BP-PHD filter are illustrated by simulation examples. The simulation results show that a box-particle implementation of the ET-PHD filter can avoid the high number of particles and reduce computational burden, compared to a particle implementation of that for extended target tracking.

Hydrodynamics of microbial filter feeding.

Science.gov (United States)

Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H; Kiørboe, Thomas; Andersen, Anders

2017-08-29

Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis , and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling.

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

International Nuclear Information System (INIS)

Kao, Jim; Flicker, Dawn; Ide, Kayo; Ghil, Michael

2006-01-01

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from a single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand

Multi-agent target tracking using particle filters enhanced with context data

CSIR Research Space (South Africa)

Claessens, R

2015-05-01

Full Text Available The proposed framework for Multi-Agent Target Tracking supports i) tracking of objects and ii) search and rescue based on the fusion of very heterogeneous data. The system is based on a novel approach to fusing sensory observations, intelligence...