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...
Power system static state estimation using Kalman filter algorithm
Directory of Open Access Journals (Sweden)
Saikia Anupam
2016-01-01
Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.
Implicit Kalman filter algorithm for nuclear reactor analysis
International Nuclear Information System (INIS)
Hassberger, J.A.; Lee, J.C.
1986-01-01
Artificial intelligence (AI) is currently the hot topic in nuclear power plant diagnostics and control. Recently, researchers have considered the use of simulation as knowledge in which faster than real-time best-estimate simulations based on first principles are tightly coupled with AI systems for analyzing power plant transients on-line. On-line simulations can be improved through a Kalman filter, a mathematical technique for obtaining the optimal estimate of a system state given the information contained in the equations of system dynamics and measurements made on the system. Filtering can be used to systemically adjust parameters of a low-order simulation model to obtain reasonable agreement between the model and actual plant dynamics. The authors present here a general Kalman filtering algorithm that derives its information of system dynamics implicitly and naturally from the discrete time step-series of state estimates available from a simulation program. Previous research has demonstrated that models adjusted on past data can be coupled with an intelligent controller to predict the future time-course of plant transients
On a New Family of Kalman Filter Algorithms for Integrated Navigation
Mahboub, V.; Saadatseresht, M.; Ardalan, A. A.
2017-09-01
Here we present a review on a new family of Kalman filter algorithms which recently developed for integrated navigation. In particular it is useful for vision based navigation due to the type of data. Here we mainly focus on three algorithms namely weighted Total Kalman filter (WTKF), integrated Kalman filter (IKF) and constrained integrated Kalman filter (CIKF). The common characteristic of these algorithms is that they can consider the neglected random observed quantities which may appear in the dynamic model. Moreover, our approach makes use of condition equations and straightforward variance propagation rules. The WTKF algorithm can deal with problems with arbitrary weight matrixes. Both of the observation equations and system equations can be dynamic-errors-in-variables (DEIV) models in the IKF algorithms. In some problems a quadratic constraint may exist. They can be solved by CIKF algorithm. Finally, we compare four algorithms WTKF, IKF, CIKF and EKF in numerical examples.
Directory of Open Access Journals (Sweden)
Qiguang Zhu
2014-05-01
Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.
GPS Signal Offset Detection and Noise Strength Estimation in a Parallel Kalman Filter Algorithm
National Research Council Canada - National Science Library
Vanek, Barry
1999-01-01
.... The variance of the noise process is estimated and provided to the second algorithm, a parallel Kalman filter structure, which then adapts to changes in the real-world measurement noise strength...
Institute of Scientific and Technical Information of China (English)
L(U) Wei-cai; XU Shao-quan
2004-01-01
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.
Raymond L. Czaplewski
2015-01-01
Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of...
Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms
Janjic, Tijana; McLaughlin, Dennis B.; Cohn, Stephen E.; Verlaan, Martin
2013-01-01
Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.
Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm
Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong
2018-06-01
The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
International Nuclear Information System (INIS)
Zheng Hong; Liu Xu; Wei Min
2015-01-01
In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. (paper)
Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms
Janjic, Tijana; Mclaughlin, Dennis; Cohn, Stephen E.; Verlaan, Martin
2014-01-01
This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.
Kalman Filtering with Real-Time Applications
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.
Hu, Shaoxing; Xu, Shike; Wang, Duhu; Zhang, Aiwu
2015-11-11
Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted "useful" data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.
Multilevel ensemble Kalman filter
Chernov, Alexey; Hoel, Haakon; Law, Kody; Nobile, Fabio; Tempone, Raul
2016-01-01
This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.
Multilevel ensemble Kalman filter
Chernov, Alexey
2016-01-06
This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.
International Nuclear Information System (INIS)
Aumeier, S.E.; Forsmann, J.H.
1998-01-01
The ability to nondestructively determine the presence and quantity of fissile/fertile nuclei in various matrices is important in several areas of nuclear applications, including international and domestic safeguards, radioactive waste characterization, and nuclear facility operations. An analysis was performed to determine the feasibility of identifying the masses of individual fissionable isotopes from a cumulative delayed-neutron signal resulting form the neutron irradiation of several uranium and plutonium isotopes. The feasibility of two separate data-processing techniques was studied: Kalman filtering and genetic algorithms. The basis of each technique is reviewed, and the structure of the algorithms as applied to the delayed-neutron analysis problem is presented. The results of parametric studies performed using several variants of the algorithms are presented. The effect of including additional constraining information such as additional measurements and known relative isotopic concentration is discussed. The parametric studies were conducted using simulated delayed-neutron data representative of the cumulative delayed-neutron response following irradiation of a sample containing 238 U, 235 U, 239 Pu, and 240 Pu. The results show that by processing delayed-neutron data representative of two significantly different fissile/fertile fission ratios, both Kalman filters and genetic algorithms are capable of yielding reasonably accurate estimates of the mass of individual isotopes contained in a given assay sample
Kalman filtering with real-time applications
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...
Institute of Scientific and Technical Information of China (English)
FENG Bo; MA Hong-Bin; FU Meng-Yin; WANG Shun-Ting
2013-01-01
Kalman filtering techniques have been widely used in many applications,however,standard Kalman filters for linear Gaussian systems usually cannot work well or even diverge in the presence of large model uncertainty.In practical applications,it is expensive to have large number of high-cost experiments or even impossible to obtain an exact system model.Motivated by our previous pioneering work on finite-model adaptive control,a framework of finite-model Kalman filtering is introduced in this paper.This framework presumes that large model uncertainty may be restricted by a finite set of known models which can be very different from each other.Moreover,the number of known models in the set can be flexibly chosen so that the uncertain model may always be approximated by one of the known models,in other words,the large model uncertainty is "covered" by the "convex hull" of the known models.Within the presented framework according to the idea of adaptive switching via the minimizing vector distance principle,a simple finite-model Kalman filter,MVDP-FMKF,is mathematically formulated and illustrated by extensive simulations.An experiment of MEMS gyroscope drift has verified the effectiveness of the proposed algorithm,indicating that the mechanism of finite-model Kalman filter is useful and efficient in practical applications of Kalman filters,especially in inertial navigation systems.
Low-cost attitude determination system using an extended Kalman filter (EKF) algorithm
Esteves, Fernando M.; Nehmetallah, Georges; Abot, Jandro L.
2016-05-01
Attitude determination is one of the most important subsystems in spacecraft, satellite, or scientific balloon mission s, since it can be combined with actuators to provide rate stabilization and pointing accuracy for payloads. In this paper, a low-cost attitude determination system with a precision in the order of arc-seconds that uses low-cost commercial sensors is presented including a set of uncorrelated MEMS gyroscopes, two clinometers, and a magnetometer in a hierarchical manner. The faster and less precise sensors are updated by the slower, but more precise ones through an Extended Kalman Filter (EKF)-based data fusion algorithm. A revision of the EKF algorithm fundamentals and its implementation to the current application, are presented along with an analysis of sensors noise. Finally, the results from the data fusion algorithm implementation are discussed in detail.
An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.
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.
DEFF Research Database (Denmark)
Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan
2006-01-01
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....
Adaptable Iterative and Recursive Kalman Filter Schemes
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Phase unwrapping algorithm using polynomial phase approximation and linear Kalman filter.
Kulkarni, Rishikesh; Rastogi, Pramod
2018-02-01
A noise-robust phase unwrapping algorithm is proposed based on state space analysis and polynomial phase approximation using wrapped phase measurement. The true phase is approximated as a two-dimensional first order polynomial function within a small sized window around each pixel. The estimates of polynomial coefficients provide the measurement of phase and local fringe frequencies. A state space representation of spatial phase evolution and the wrapped phase measurement is considered with the state vector consisting of polynomial coefficients as its elements. Instead of using the traditional nonlinear Kalman filter for the purpose of state estimation, we propose to use the linear Kalman filter operating directly with the wrapped phase measurement. The adaptive window width is selected at each pixel based on the local fringe density to strike a balance between the computation time and the noise robustness. In order to retrieve the unwrapped phase, either a line-scanning approach or a quality guided strategy of pixel selection is used depending on the underlying continuous or discontinuous phase distribution, respectively. Simulation and experimental results are provided to demonstrate the applicability of the proposed method.
Phase Center Interpolation Algorithm for Airborne GPS through the Kalman Filter
Directory of Open Access Journals (Sweden)
Edson A. Mitishita
2005-12-01
Full Text Available The aerial triangulation is a fundamental step in any photogrammetric project. The surveying of the traditional control points, depending on region to be mapped, still has a high cost. The distribution of control points at the block, and its positional quality, influence directly in the resulting precisions of the aero triangulation processing. The airborne GPS technique has as key objectives cost reduction and quality improvement of the ground control in the modern photogrammetric projects. Nowadays, in Brazil, the greatest photogrammetric companies are acquiring airborne GPS systems, but those systems are usually presenting difficulties in the operation, due to the need of human resources for the operation, because of the high technology involved. Inside the airborne GPS technique, one of the fundamental steps is the interpolation of the position of the phase center of the GPS antenna, in the photo shot instant. Traditionally, low degree polynomials are used, but recent studies show that those polynomials is reduced in turbulent flights, which are quite common, mainly in great scales flights. This paper has as objective to present a solution for that problem, through an algorithm based on the Kalman Filter, which takes into account the dynamic aspect of the problem. At the end of the paper, the results of a comparison between experiments done with the proposed methodology and a common linear interpolator are shown. These results show a significant accuracy gain at the procedure of linear interpolation, when the Kalman filter is used.
Time scale algorithm: Definition of ensemble time and possible uses of the Kalman filter
Tavella, Patrizia; Thomas, Claudine
1990-01-01
The comparative study of two time scale algorithms, devised to satisfy different but related requirements, is presented. They are ALGOS(BIPM), producing the international reference TAI at the Bureau International des Poids et Mesures, and AT1(NIST), generating the real-time time scale AT1 at the National Institute of Standards and Technology. In each case, the time scale is a weighted average of clock readings, but the weight determination and the frequency prediction are different because they are adapted to different purposes. The possibility of using a mathematical tool, such as the Kalman filter, together with the definition of the time scale as a weighted average, is also analyzed. Results obtained by simulation are presented.
Sprenger, D; Adolphi, R; Brauer, R; Feld, L; Klein, K; Ostaptchuk, A; Schael, S; Wittmer, B
2010-01-01
A Kalman Filter alignment algorithm has been applied to cosmic-ray data. We discuss the alignment algorithm and an experiment-independent implementation including outlier rejection and treatment of weakly determined parameters. Using this implementation, the algorithm has been applied to data recorded with one CMS silicon tracker endcap. Results are compared to both photogrammetry measurements and data obtained from a dedicated hardware alignment system, and good agreement is observed.
Multilevel ensemble Kalman filtering
Hoel, Haakon
2016-01-08
The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.
Multilevel ensemble Kalman filtering
Hoel, Haakon; Chernov, Alexey; Law, Kody; Nobile, Fabio; Tempone, Raul
2016-01-01
The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.
Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning
Directory of Open Access Journals (Sweden)
CHEN GuoLiang
2015-12-01
Full Text Available Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning positioning with UKF (unscented Kalman filter. Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy.
Tracking speckle displacement by double Kalman filtering
Institute of Scientific and Technical Information of China (English)
Donghui Li; Li Guo
2006-01-01
@@ A tracking technique using two sequentially-connected Kalman filter for tracking laser speckle displacement is presented. One Kalman filter tracks temporal speckle displacement, while another Kalman filter tracks spatial speckle displacement. The temporal Kalman filter provides a prior for the spatial Kalman filter, and the spatial Kalman filter provides measurements for the temporal Kalman filter. The contribution of a prior to estimations of the spatial Kalman filter is analyzed. An optical analysis system was set up to verify the double-Kalman-filter tracker's ability of tracking laser speckle's constant displacement.
Valdes-Abellan, Javier; Pachepsky, Yakov; Martinez, Gonzalo
2018-01-01
Data assimilation is becoming a promising technique in hydrologic modelling to update not only model states but also to infer model parameters, specifically to infer soil hydraulic properties in Richard-equation-based soil water models. The Ensemble Kalman Filter method is one of the most widely employed method among the different data assimilation alternatives. In this study the complete Matlab© code used to study soil data assimilation efficiency under different soil and climatic conditions is shown. The code shows the method how data assimilation through EnKF was implemented. Richards equation was solved by the used of Hydrus-1D software which was run from Matlab. •MATLAB routines are released to be used/modified without restrictions for other researchers•Data assimilation Ensemble Kalman Filter method code.•Soil water Richard equation flow solved by Hydrus-1D.
Kalman filter-based gap conductance modeling
International Nuclear Information System (INIS)
Tylee, J.L.
1983-01-01
Geometric and thermal property uncertainties contribute greatly to the problem of determining conductance within the fuel-clad gas gap of a nuclear fuel pin. Accurate conductance values are needed for power plant licensing transient analysis and for test analyses at research facilities. Recent work by Meek, Doerner, and Adams has shown that use of Kalman filters to estimate gap conductance is a promising approach. A Kalman filter is simply a mathematical algorithm that employs available system measurements and assumed dynamic models to generate optimal system state vector estimates. This summary addresses another Kalman filter approach to gap conductance estimation and subsequent identification of an empirical conductance model
Multilevel ensemble Kalman filtering
Hoel, Hakon
2016-06-14
This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
Multilevel ensemble Kalman filtering
Hoel, Hakon; Law, Kody J. H.; Tempone, Raul
2016-01-01
This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
Mixtures of skewed Kalman filters
Kim, Hyoungmoon; Ryu, Duchwan; Mallick, Bani K.; Genton, Marc G.
2014-01-01
Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
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.
MR fingerprinting reconstruction with Kalman filter.
Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping
2017-09-01
Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*
Hoteit, Ibrahim
2012-02-01
This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.
Q-Method Extended Kalman Filter
Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.
2012-01-01
A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.
Reduced Kalman Filters for Clock Ensembles
Greenhall, Charles A.
2011-01-01
This paper summarizes the author's work ontimescales based on Kalman filters that act upon the clock comparisons. The natural Kalman timescale algorithm tends to optimize long-term timescale stability at the expense of short-term stability. By subjecting each post-measurement error covariance matrix to a non-transparent reduction operation, one obtains corrected clocks with improved short-term stability and little sacrifice of long-term stability.
Directory of Open Access Journals (Sweden)
Lei Wang
2017-01-01
Full Text Available For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF on low cost Micro-Electro-Mechanical System (MEMS gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU. The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.
Cheng, Xuemin; Hao, Qun; Xie, Mengdi
2016-04-07
Video stabilization is an important technology for removing undesired motion in videos. This paper presents a comprehensive motion estimation method for electronic image stabilization techniques, integrating the speeded up robust features (SURF) algorithm, modified random sample consensus (RANSAC), and the Kalman filter, and also taking camera scaling and conventional camera translation and rotation into full consideration. Using SURF in sub-pixel space, feature points were located and then matched. The false matched points were removed by modified RANSAC. Global motion was estimated by using the feature points and modified cascading parameters, which reduced the accumulated errors in a series of frames and improved the peak signal to noise ratio (PSNR) by 8.2 dB. A specific Kalman filter model was established by considering the movement and scaling of scenes. Finally, video stabilization was achieved with filtered motion parameters using the modified adjacent frame compensation. The experimental results proved that the target images were stabilized even when the vibrating amplitudes of the video become increasingly large.
Algoritma Filter Kalman untuk Menghaluskan Data Pengukuran
Rudiyanto; Setiawan, Budi Indra; Saptomo, Satyanto Krido
2006-01-01
The objective of this paper is to apply a simple algorithm of Kalman Filter, wich is know as noise data filtering. The computer program was written in Macro Visual Basic in MS Exel. Testings were carried out on available temperature, Water level and force data and then were comared with the mooving average method. The result shows that the algorithm performed better and lesser deviation than the mooving average.
Algoritma Filter Kalman untuk Menghaluskan Data Pengukuran
Directory of Open Access Journals (Sweden)
Rudiyanto
2006-12-01
Full Text Available The objective of this paper is to apply a simple algorithm of Kalman Filter, wich is know as noise data filtering. The computer program was written in Macro Visual Basic in MS Exel. Testings were carried out on available temperature, Water level and force data and then were comared with the mooving average method. The result shows that the algorithm performed better and lesser deviation than the mooving average.
DEFF Research Database (Denmark)
Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth
2016-01-01
Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve......-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing...
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Hoteit, Ibrahim
2010-09-19
Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.
RAPID TRANSFER ALIGNMENT USING FEDERATED KALMAN FILTER
Institute of Scientific and Technical Information of China (English)
GUDong-qing; QINYong-yuan; PENGRong; LIXin
2005-01-01
The dimension number of the centralized Kalman filter (CKF) for the rapid transfer alignment (TA) is as high as 21 if the aircraft wing flexure motion is considered in the rapid TA. The 21-dimensional CKF brings the calculation burden on the computer and the difficulty to meet a high filtering updating rate desired by rapid TA. The federated Kalman filter (FKF) for the rapid TA is proposed to solve the dilemma. The structure and the algorithm of the FKF, which can perform parallel computation and has less calculation burden, are designed.The wing flexure motion is modeled, and then the 12-order velocity matching local filter and the 15-order attitud ematching local filter are devised. Simulation results show that the proposed EKE for the rapid TA almost has the same performance as the CKF. Thus the calculation burden of the proposed FKF for the rapid TA is markedly decreased.
A Kalman filter-based short baseline RTK algorithm for single-frequency combination of GPS and BDS.
Zhao, Sihao; Cui, Xiaowei; Guan, Feng; Lu, Mingquan
2014-08-20
The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.
A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS
Directory of Open Access Journals (Sweden)
Sihao Zhao
2014-08-01
Full Text Available The emerging Global Navigation Satellite Systems (GNSS including the BeiDou Navigation Satellite System (BDS offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.
Adaptive Federal Kalman Filtering for SINS/GPS Integrated System
Institute of Scientific and Technical Information of China (English)
杨勇; 缪玲娟
2003-01-01
A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.
Directory of Open Access Journals (Sweden)
Xin Li
2016-02-01
Full Text Available Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF.
Spectral Diagonal Ensemble Kalman Filters
Czech Academy of Sciences Publication Activity Database
Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin
2015-01-01
Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS-1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015
Star-sensor-based predictive Kalman filter for satelliteattitude estimation
Institute of Scientific and Technical Information of China (English)
林玉荣; 邓正隆
2002-01-01
A real-time attitude estimation algorithm, namely the predictive Kalman filter, is presented. This algorithm can accurately estimate the three-axis attitude of a satellite using only star sensor measurements. The implementation of the filter includes two steps: first, predicting the torque modeling error, and then estimating the attitude. Simulation results indicate that the predictive Kalman filter provides robust performance in the presence of both significant errors in the assumed model and in the initial conditions.
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.
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.
Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun
2017-09-19
In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Hoteit, Ibrahim; Luo, Xiaodong; Pham, Dinh-Tuan; Moroz, Irene M.
2010-01-01
In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.
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...
Data assimilation in the early phase: Kalman filtering RIMPUFF
DEFF Research Database (Denmark)
Astrup, P.; Turcanu, C.; Puch, R.O.
2004-01-01
of RODOS (Realtime Online DecisiOn Support system for nuclear emergencies) – has been developed. It is built on the Kalman filtering algorithm and it assimilates 10-minute averaged gamma dose rates measured atground level stations. Since the gamma rates are non-linear functions of the state vector...... variables, the applied Kalman filter is the so-called Extended Kalman filter. In more ways the implementation is non standard: 1) the number of state vectorvariables varies with time, and 2) the state vector variables are prediction updated with 1-minute time steps but only Kalman filtered every 10 minutes......, and this based on time averaged measurements. Given reasonable conditions, i.e. a spatially densedistribution of gamma monitors and a realistic wind field, the developed ADUM module is found to be able to enhance the prediction of the gamma dose field. Based on some of the Kalman filtering parameters, another...
Mixtures of skewed Kalman filters
Kim, Hyoungmoon
2014-01-01
Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic properties are derived and a class of closed skew. t distributions is obtained. Our suggested family of distributions is skewed and has heavy tails too, so it is appropriate for robust analysis. Our proposed special sequential Monte Carlo methods use a random mixture of the closed skew-normal distributions to approximate a target distribution. Hence it is possible to handle skewed and heavy tailed data simultaneously. These methods are illustrated with numerical experiments. © 2013 Elsevier Inc.
Spectral Ensemble Kalman Filters
Czech Academy of Sciences Publication Activity Database
Mandel, Jan; Kasanický, Ivan; Vejmelka, Martin; Fuglík, Viktor; Turčičová, Marie; Eben, Kryštof; Resler, Jaroslav; Juruš, Pavel
2014-01-01
Roč. 11, - (2014), EMS2014-446 [EMS Annual Meeting /14./ & European Conference on Applied Climatology (ECAC) /10./. 06.10.2014-10.10.2014, Prague] R&D Projects: GA ČR GA13-34856S Grant - others:NSF DMS-1216481 Institutional support: RVO:67985807 Keywords : data assimilation * spectral filter Subject RIV: DG - Athmosphere Sciences, Meteorology
A quantum extended Kalman filter
International Nuclear Information System (INIS)
Emzir, Muhammad F; Woolley, Matthew J; Petersen, Ian R
2017-01-01
In quantum physics, a stochastic master equation (SME) estimates the state (density operator) of a quantum system in the Schrödinger picture based on a record of measurements made on the system. In the Heisenberg picture, the SME is a quantum filter. For a linear quantum system subject to linear measurements and Gaussian noise, the dynamics may be described by quantum stochastic differential equations (QSDEs), also known as quantum Langevin equations, and the quantum filter reduces to a so-called quantum Kalman filter. In this article, we introduce a quantum extended Kalman filter (quantum EKF), which applies a commutative approximation and a time-varying linearization to systems of nonlinear QSDEs. We will show that there are conditions under which a filter similar to a classical EKF can be implemented for quantum systems. The boundedness of estimation errors and the filtering problem with ‘state-dependent’ covariances for process and measurement noises are also discussed. We demonstrate the effectiveness of the quantum EKF by applying it to systems that involve multiple modes, nonlinear Hamiltonians, and simultaneous jump-diffusive measurements. (paper)
A quantum extended Kalman filter
Emzir, Muhammad F.; Woolley, Matthew J.; Petersen, Ian R.
2017-06-01
In quantum physics, a stochastic master equation (SME) estimates the state (density operator) of a quantum system in the Schrödinger picture based on a record of measurements made on the system. In the Heisenberg picture, the SME is a quantum filter. For a linear quantum system subject to linear measurements and Gaussian noise, the dynamics may be described by quantum stochastic differential equations (QSDEs), also known as quantum Langevin equations, and the quantum filter reduces to a so-called quantum Kalman filter. In this article, we introduce a quantum extended Kalman filter (quantum EKF), which applies a commutative approximation and a time-varying linearization to systems of nonlinear QSDEs. We will show that there are conditions under which a filter similar to a classical EKF can be implemented for quantum systems. The boundedness of estimation errors and the filtering problem with ‘state-dependent’ covariances for process and measurement noises are also discussed. We demonstrate the effectiveness of the quantum EKF by applying it to systems that involve multiple modes, nonlinear Hamiltonians, and simultaneous jump-diffusive measurements.
Erna Apriliani; Dieky Adzkiya; Arief Baihaqi
2011-01-01
Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ense...
Chen, Guoliang; Meng, Xiaolin; Wang, Yunjia; Zhang, Yanzhe; Tian, Peng; Yang, Huachao
2015-09-23
Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.
Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization
Directory of Open Access Journals (Sweden)
Guoliang Chen
2015-09-01
Full Text Available Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.
LHCb Kalman Filter cross architecture studies
Hugo, Daniel; Pérez, Cámpora
2017-10-01
The 2020 upgrade of the LHCb detector will vastly increase the rate of collisions the Online system needs to process in software, in order to filter events in real time. 30 million collisions per second will pass through a selection chain, where each step is executed conditional to its prior acceptance. The Kalman Filter is a fit applied to all reconstructed tracks which, due to its time characteristics and early execution in the selection chain, consumes 40% of the whole reconstruction time in the current trigger software. This makes the Kalman Filter a time-critical component as the LHCb trigger evolves into a full software trigger in the Upgrade. I present a new Kalman Filter algorithm for LHCb that can efficiently make use of any kind of SIMD processor, and its design is explained in depth. Performance benchmarks are compared between a variety of hardware architectures, including x86_64 and Power8, and the Intel Xeon Phi accelerator, and the suitability of said architectures to efficiently perform the LHCb Reconstruction process is determined.
The Kalman Filter Revisited Using Maximum Relative Entropy
Directory of Open Access Journals (Sweden)
Adom Giffin
2014-02-01
Full Text Available In 1960, Rudolf E. Kalman created what is known as the Kalman filter, which is a way to estimate unknown variables from noisy measurements. The algorithm follows the logic that if the previous state of the system is known, it could be used as the best guess for the current state. This information is first applied a priori to any measurement by using it in the underlying dynamics of the system. Second, measurements of the unknown variables are taken. These two pieces of information are taken into account to determine the current state of the system. Bayesian inference is specifically designed to accommodate the problem of updating what we think of the world based on partial or uncertain information. In this paper, we present a derivation of the general Bayesian filter, then adapt it for Markov systems. A simple example is shown for pedagogical purposes. We also show that by using the Kalman assumptions or “constraints”, we can arrive at the Kalman filter using the method of maximum (relative entropy (MrE, which goes beyond Bayesian methods. Finally, we derive a generalized, nonlinear filter using MrE, where the original Kalman Filter is a special case. We further show that the variable relationship can be any function, and thus, approximations, such as the extended Kalman filter, the unscented Kalman filter and other Kalman variants are special cases as well.
Institute of Scientific and Technical Information of China (English)
许光辉; 胡光锐
2005-01-01
A new Kalman filtering algorithm based on estimation of spread spectrum signal before suppression of narrowband interference (NBI) in spread spectrum systems, using the dependence of autoregressive (AR) interference, is presented compared with performance of the ACM nonlinear filtering algorithm, simulation results show that the proposed algorithm has preferable performance, there is about 5 dB SNR improvement in average.
The development rainfall forecasting using kalman filter
Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala
2018-04-01
Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.
Robotic fish tracking method based on suboptimal interval Kalman filter
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.
Kalman filter tracking on parallel architectures
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.
Extended Kalman Filter Modifications Based on an Optimization View Point
Skoglund, Martin; Hendeby, Gustaf; Axehill, Daniel
2015-01-01
The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the it...
A Tool for Kalman Filter Tuning
DEFF Research Database (Denmark)
Åkesson, Bernt Magnus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad
2007-01-01
The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances......, which can be used to compute the Kalman filter gain....
The AGILE on-board Kalman filter
International Nuclear Information System (INIS)
Giuliani, A.; Cocco, V.; Mereghetti, S.; Pittori, C.; Tavani, M.
2006-01-01
On-board reduction of particle background is one of the main challenges of space instruments dedicated to gamma-ray astrophysics. We present in this paper a discussion of the method and main simulation results of the on-board background filter of the Gamma-Ray Imaging Detector (GRID) of the AGILE mission. The GRID is capable of detecting and imaging with optimal point spread function gamma-ray photons in the range 30MeV-30GeV. The AGILE planned orbit is equatorial, with an altitude of 550km. This is an optimal orbit from the point of view of the expected particle background. For this orbit, electrons and positrons of kinetic energies between 20MeV and hundreds of MeV dominate the particle background, with significant contributions from high-energy (primary) and low-energy protons, and gamma-ray albedo-photons. We present here the main results obtained by extensive simulations of the on-board AGILE-GRID particle/photon background rejection algorithms based on a special application of Kalman filter techniques. This filter is applied (Level-2) sequentially after other data processing techniques characterizing the Level-1 processing. We show that, in conjunction with the Level-1 processing, the adopted Kalman filtering is expected to reduce the total particle/albedo-photon background rate to a value (=<10-30Hz) that is compatible with the AGILE telemetry. The AGILE on-board Kalman filter is also effective in reducing the Earth-albedo-photon background rate, and therefore contributes to substantially increase the AGILE exposure for celestial gamma-ray sources
Machine learning of radial basis function neural network based on Kalman filter: Introduction
Directory of Open Access Journals (Sweden)
Vuković Najdan L.
2014-01-01
Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.
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
Estimation of Sideslip Angle Based on Extended Kalman Filter
Directory of Open Access Journals (Sweden)
Yupeng Huang
2017-01-01
Full Text Available The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.
Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation
Simon, Dan; Simon, Donald L.
2005-01-01
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.
An Unbiased Unscented Transform Based Kalman Filter for 3D Radar
Institute of Scientific and Technical Information of China (English)
WANGGuohong; XIUJianjuan; HEYou
2004-01-01
As a derivative-free alternative to the Extended Kalman filter (EKF) in the framework of state estimation, the Unscented Kalman filter (UKF) has potential applications in nonlinear filtering. By noting the fact that the unscented transform is generally biased when converting the radar measurements from spherical coordinates into Cartesian coordinates, a new filtering algorithm for 3D radar, called Unbiased unscented Kalman filter (UUKF), is proposed. The new algorithm is validated by Monte Carlo simulation runs. Simulation results show that the UUKF is more effective than the UKF, EKF and the Converted measurement Kalman filter (CMKF).
International Nuclear Information System (INIS)
Ermolaev, P; Volynsky, M
2014-01-01
Recurrent stochastic data processing algorithms using representation of interferometric signal as output of a dynamic system, which state is described by vector of parameters, in some cases are more effective, compared with conventional algorithms. Interferometric signals depend on phase nonlinearly. Consequently it is expedient to apply algorithms of nonlinear stochastic filtering, such as Kalman type filters. An application of the second order extended Kalman filter and Markov nonlinear filter that allows to minimize estimation error is described. Experimental results of signals processing are illustrated. Comparison of the algorithms is presented and discussed.
Unscented Kalman filter for SINS alignment
Institute of Scientific and Technical Information of China (English)
Zhou Zhanxin; Gao Yanan; Chen Jiabin
2007-01-01
In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment.Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in cases of large initial misalignment.The UKF has good performance in case of small initial misalignment.
CSIR Research Space (South Africa)
Salmon
2012-07-01
Full Text Available stream_source_info Salmon1_2012_ABSTRACT ONLY.pdf.txt stream_content_type text/plain stream_size 1654 Content-Encoding ISO-8859-1 stream_name Salmon1_2012_ABSTRACT ONLY.pdf.txt Content-Type text/plain; charset=ISO-8859...-1 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012 A search algorithm to meta-optimize the parameters for an extended Kalman filter to improve classification on hyper-temporal images yzB.P. Salmon, yz...
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.
Chowdhury, Amor; Sarjaš, Andrej
2016-09-15
The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.
Boundary Value Problems Arising in Kalman Filtering
Directory of Open Access Journals (Sweden)
Sinem Ertürk
2009-01-01
Full Text Available The classic Kalman filtering equations for independent and correlated white noises are ordinary differential equations (deterministic or stochastic with the respective initial conditions. Changing the noise processes by taking them to be more realistic wide band noises or delayed white noises creates challenging partial differential equations with initial and boundary conditions. In this paper, we are aimed to give a survey of this connection between Kalman filtering and boundary value problems, bringing them into the attention of mathematicians as well as engineers dealing with Kalman filtering and boundary value problems.
Boundary Value Problems Arising in Kalman Filtering
Directory of Open Access Journals (Sweden)
Bashirov Agamirza
2008-01-01
Full Text Available The classic Kalman filtering equations for independent and correlated white noises are ordinary differential equations (deterministic or stochastic with the respective initial conditions. Changing the noise processes by taking them to be more realistic wide band noises or delayed white noises creates challenging partial differential equations with initial and boundary conditions. In this paper, we are aimed to give a survey of this connection between Kalman filtering and boundary value problems, bringing them into the attention of mathematicians as well as engineers dealing with Kalman filtering and boundary value problems.
Kalman filter to update forest cover estimates
Raymond L. Czaplewski
1990-01-01
The Kalman filter is a statistical estimator that combines a time-series of independent estimates, using a prediction model that describes expected changes in the state of a system over time. An expensive inventory can be updated using model predictions that are adjusted with more recent, but less expensive and precise, monitoring data. The concepts of the Kalman...
Ensemble Kalman filtering with residual nudging
Luo, X.; Hoteit, Ibrahim
2012-01-01
Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work
Industrial applications of the Kalman filter
DEFF Research Database (Denmark)
Auger, François; Hilairet, Mickael; Guerrero, Josep M.
2013-01-01
The Kalman filter has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 70s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal...... processing and so on. This paper provides a brief overview of the industrial applications and implementation issues of the Kalman filter in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends....
Institute of Scientific and Technical Information of China (English)
叶军; 陈坚; 石国祥
2011-01-01
针对自标定加速度计组合动基座试验数据中存在的数据异常问题,推导并运用自适应Kalman滤波算法剔除异常数据,通过对不同Kalman滤波算法自标定精度解算结果的均值和标准差进行比较,表明自适应Kalman滤波算法更加有效.%Aiming at the problems of abnormal data in the test data of autonomous calibration accelerometer-unit on dynamicbase,deducing and using adaptive Kalman filtering algorithm eliminates abnormal data, according the comparison of results from calibration precision by different Kalman filtering algorithm, it shows that the adaptive Kalman filtering algorithm is more effective.
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.
Design of Kalman filters for mobile robots
DEFF Research Database (Denmark)
Larsen, Thomas Dall; Hansen, Karsten L.; Andersen, Nils Axel
1999-01-01
the mobile robot is equipped with a dual encoder system supported by some additional absolute measurements. A common filter type for this setup is the odometric filter, where readings from the odometry system on the robot are used together with the geometry of the robot movement as a model of the robot......Kalman filters have for a long time been widely used on mobile robots as a location estimator. Many different Kalman filter designs have been proposed, using models of various complexity. In this paper, two different design methods are evaluated and compared. Focus is put on the common setup where...... estimates. The Kalman filter normally consists of a time update followed by one or more data updates. However, it is shown that when using the kinematic filter, the encoder measurements should be fused prior to the time update for better performance....
Neural network training by Kalman filtering in process system monitoring
International Nuclear Information System (INIS)
Ciftcioglu, Oe.
1996-03-01
Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)
Harmonic Detection at Initialization With Kalman Filter
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Imran, Raja Muhammad; Shoro, Ghulam Mustafa
2014-01-01
Most power electronic equipment these days generate harmonic disturbances, these devices hold nonlinear voltage/current characteristic. The harmonics generated can potentially be harmful to the consumer supply. Typically, filters are integrated at the power source or utility location to filter out...... the affect of harmonics on the supply. For the detection of these harmonics various techniques are available and one of that technique is the Kalman filter. In this paper we investigate that what are the consequences when harmonic detection system based on Kalman Filtering is initialized...
Directory of Open Access Journals (Sweden)
Jianhua Cheng
2017-10-01
Full Text Available Because of the harsh polar environment, the master strapdown inertial navigation system (SINS has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.
Cheng, Jianhua; Wang, Tongda; Wang, Lu; Wang, Zhenmin
2017-10-23
Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.
International Nuclear Information System (INIS)
Wang, Tongda; Cheng, Jianhua; Guan, Dongxue; Kang, Yingyao; Zhang, Wei
2017-01-01
Due to the lever-arm effect and flexural deformation in the practical application of transfer alignment (TA), the TA performance is decreased. The existing polar TA algorithm only compensates a fixed lever-arm without considering the dynamic lever-arm caused by flexural deformation; traditional non-polar TA algorithms also have some limitations. Thus, the performance of existing compensation algorithms is unsatisfactory. In this paper, a modified compensation algorithm of the lever-arm effect and flexural deformation is proposed to promote the accuracy and speed of the polar TA. On the basis of a dynamic lever-arm model and a noise compensation method for flexural deformation, polar TA equations are derived in grid frames. Based on the velocity-plus-attitude matching method, the filter models of polar TA are designed. An adaptive Kalman filter (AKF) is improved to promote the robustness and accuracy of the system, and then applied to the estimation of the misalignment angles. Simulation and experiment results have demonstrated that the modified compensation algorithm based on the improved AKF for polar TA can effectively compensate the lever-arm effect and flexural deformation, and then improve the accuracy and speed of TA in the polar region. (paper)
Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms
Aman, Beshir M.
2012-01-01
Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step
Rachmawati, Vimala; Khusnul Arif, Didik; Adzkiya, Dieky
2018-03-01
The systems contained in the universe often have a large order. Thus, the mathematical model has many state variables that affect the computation time. In addition, generally not all variables are known, so estimations are needed to measure the magnitude of the system that cannot be measured directly. In this paper, we discuss the model reduction and estimation of state variables in the river system to measure the water level. The model reduction of a system is an approximation method of a system with a lower order without significant errors but has a dynamic behaviour that is similar to the original system. The Singular Perturbation Approximation method is one of the model reduction methods where all state variables of the equilibrium system are partitioned into fast and slow modes. Then, The Kalman filter algorithm is used to estimate state variables of stochastic dynamic systems where estimations are computed by predicting state variables based on system dynamics and measurement data. Kalman filters are used to estimate state variables in the original system and reduced system. Then, we compare the estimation results of the state and computational time between the original and reduced system.
Subspace System Identification of the Kalman Filter
Directory of Open Access Journals (Sweden)
David Di Ruscio
2003-07-01
Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.
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 Artificial Neural Network Forecasts with Kalman Filtering ...
African Journals Online (AJOL)
In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...
Iterated unscented Kalman filter for phase unwrapping of interferometric fringes.
Xie, Xianming
2016-08-22
A fresh phase unwrapping algorithm based on iterated unscented Kalman filter is proposed to estimate unambiguous unwrapped phase of interferometric fringes. This method is the result of combining an iterated unscented Kalman filter with a robust phase gradient estimator based on amended matrix pencil model, and an efficient quality-guided strategy based on heap sort. The iterated unscented Kalman filter that is one of the most robust methods under the Bayesian theorem frame in non-linear signal processing so far, is applied to perform simultaneously noise suppression and phase unwrapping of interferometric fringes for the first time, which can simplify the complexity and the difficulty of pre-filtering procedure followed by phase unwrapping procedure, and even can remove the pre-filtering procedure. The robust phase gradient estimator is used to efficiently and accurately obtain phase gradient information from interferometric fringes, which is needed for the iterated unscented Kalman filtering phase unwrapping model. The efficient quality-guided strategy is able to ensure that the proposed method fast unwraps wrapped pixels along the path from the high-quality area to the low-quality area of wrapped phase images, which can greatly improve the efficiency of phase unwrapping. Results obtained from synthetic data and real data show that the proposed method can obtain better solutions with an acceptable time consumption, with respect to some of the most used algorithms.
Data assimilation in the early phase: Kalman filtering RIMPUFF
International Nuclear Information System (INIS)
Astrup, P.; Turcanu, C.; Puch, R.O.; Palma, C.R.; Mikkelsen, T.
2004-09-01
In the framework of the DAONEM project (Data Assimilation for Off-site Nuclear Emergency Management), a data assimilation module, ADUM (Atmospheric Dispersion Updating Module), for the mesoscale atmospheric dispersion program RIMPUFF (Risoe Mesoscale Puff model) part of the early-phase programs of RODOS (Realtime Online DecisiOn Support system for nuclear emergencies) has been developed. It is built on the Kalman filtering algorithm and it assimilates 10-minute averaged gamma dose rates measured at ground level stations. Since the gamma rates are non-linear functions of the state vector variables, the applied Kalman filter is the so-called Extended Kalman filter. In more ways the implementation is non standard: 1) the number of state vector variables varies with time, and 2) the state vector variables are prediction updated with 1-minute time steps but only Kalman filtered every 10 minutes, and this based on time averaged measurements. Given reasonable conditions, i.e. a spatially dense distribution of gamma monitors and a realistic wind field, the developed ADUM module is found to be able to enhance the prediction of the gamma dose field. Based on some of the Kalman filtering parameters, another module, ToDeMM, has been developed for providing the late-phase DeMM (Deposition Monitoring Module) of RODOS with an ensemble of fields of ground level air concentrations and wet deposited material. This accounts for the uncertainty estimation of this kind of quantities as calculated by RIMPUFF for use by DeMM. (au)
A numerical storm surge forecast model with Kalman filter
Institute of Scientific and Technical Information of China (English)
Yu Fujiang; Zhang Zhanhai; Lin Yihua
2001-01-01
Kalman filter data assimilation technique is incorporated into a standard two-dimensional linear storm surge model. Imperfect model equation and imperfect meteorological forcimg are accounted for by adding noise terms to the momentum equations. The deterministic model output is corrected by using the available tidal gauge station data. The stationary Kalman filter algorithm for the model domain is calculated by an iterative procedure using specified information on the inaccuracies in the momentum equations and specified error information for the observations. An application to a real storm surge that occurred in the summer of 1956 in the East China Sea is performed by means of this data assimilation technique. The result shows that Kalman filter is useful for storm surge forecast and hindcast.
2016-06-01
Filter Algorithms for Stationary, Low-Dynamics, and High-Dynamics Applications Executive Summary The Global Positioning system ( GPS ) is the primary...software that may need to be developed for performance prediction of current or future systems that incorporate GPS . The ultimate aim is to help inform...Defence Science and Technology Organisation in 1986. His major areas of work were adaptive tracking , sig- nal processing, and radar systems engineering
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.
Directory of Open Access Journals (Sweden)
Xiaoyu Li
2015-07-01
Full Text Available This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS model is utilized to describe the battery characteristics. Dynamic stress test (DST and fractional joint Kalman filter (FJKF are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs and screening of aged batteries for recycling.
Novelli, Antonio
2016-08-01
Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties. The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.
Adaptive robust Kalman filtering for precise point positioning
International Nuclear Information System (INIS)
Guo, Fei; Zhang, Xiaohong
2014-01-01
The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises. (paper)
Restricted Kalman Filtering Theory, Methods, and Application
Pizzinga, Adrian
2012-01-01
In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where th
A new iterative speech enhancement scheme based on Kalman filtering
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2005-01-01
for a high temporal resolution estimation of this variance. A Local Variance Estimator based on a Prediction Error Kalman Filter is designed for this high temporal resolution variance estimation. To achieve fast convergence and avoid local maxima of the likelihood function, a Weighted Power Spectral....... Performance comparison shows significant improvement over the baseline EM algorithm in terms of three objective measures. Listening test indicates an improvement in subjective quality due to a significant reduction of musical noise compared to the baseline EM algorithm....
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2017-08-01
Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
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....
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Directory of Open Access Journals (Sweden)
Chien-Hao Tseng
2016-07-01
Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.
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.
Towards self-organizing Kalman filters
Sijs, J.; Papp, Z.
2012-01-01
Distributed Kalman filtering is an important signal processing method for state estimation in large-scale sensor networks. However, existing solutions do not account for unforeseen events that are likely to occur and thus dramatically changing the operational conditions (e.g. node failure,
Bayesian fault detection and isolation using Field Kalman Filter
Baranowski, Jerzy; Bania, Piotr; Prasad, Indrajeet; Cong, Tian
2017-12-01
Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety of methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman filter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a system. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be expressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault catalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of algorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed algorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed and compared with traditional EKF.
Directory of Open Access Journals (Sweden)
Xiangwei Guo
2016-02-01
Full Text Available An estimation of the power battery state of charge (SOC is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS with a forgetting factor and the adaptive unscented Kalman filter (AUKF algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.
Design considerations for a suboptimal Kalman filter
Difilippo, D. J.
1995-06-01
In designing a suboptimal Kalman filter, the designer must decide how to simplify the system error model without causing the filter estimation errors to increase to unacceptable levels. Deletion of certain error states and decoupling of error state dynamics are the two principal model simplifications that are commonly used in suboptimal filter design. For the most part, the decisions as to which error states can be deleted or decoupled are based on the designer's understanding of the physics of the particular system. Consequently, the details of a suboptimal design are usually unique to the specific application. In this paper, the process of designing a suboptimal Kalman filter is illustrated for the case of an airborne transfer-of-alignment (TOA) system used for synthetic aperture radar (SAR) motion compensation. In this application, the filter must continuously transfer the alignment of an onboard Doppler-damped master inertial navigation system (INS) to a strapdown navigator that processes information from a less accurate inertial measurement unit (IMU) mounted on the radar antenna. The IMU is used to measure spurious antenna motion during the SAR imaging interval, so that compensating phase corrections can be computed and applied to the radar returns, thereby presenting image degradation that would otherwise result from such motions. The principles of SAR are described in many references, for instance. The primary function of the TOA Kalman filter in a SAR motion compensation system is to control strapdown navigator attitude errors, and to a less degree, velocity and heading errors. Unlike a classical navigation application, absolute positional accuracy is not important. The motion compensation requirements for SAR imaging are discussed in some detail. This TOA application is particularly appropriate as a vehicle for discussing suboptimal filter design, because the system contains features that can be exploited to allow both deletion and decoupling of error
Steady-State Performance of Kalman Filter for DPLL
Institute of Scientific and Technical Information of China (English)
QIAN Yi; CUI Xiaowei; LU Mingquan; FENG Zhenming
2009-01-01
For certain system models, the structure of the Kalman filter is equivalent to a second-order vari-able gain digital phase-locked loop (DPLL). To apply the knowledge of DPLLs to the design of Kalman filters, this paper studies the steady-state performance of Kalman filters for these system models. The results show that the steady-state Kalman gain has the same form as the DPLL gain. An approximate simple form for the steady-state Kalman gain is used to derive an expression for the equivalent loop bandwidth of the Kalman filter as a function of the process and observation noise variances. These results can be used to analyze the steady-state performance of a Kalman filter with DPLL theory or to design a Kalman filter model with the same steady-state performance as a given DPLL.
Fire spread estimation on forest wildfire using ensemble kalman filter
Syarifah, Wardatus; Apriliani, Erna
2018-04-01
Wildfire is one of the most frequent disasters in the world, for example forest wildfire, causing population of forest decrease. Forest wildfire, whether naturally occurring or prescribed, are potential risks for ecosystems and human settlements. These risks can be managed by monitoring the weather, prescribing fires to limit available fuel, and creating firebreaks. With computer simulations we can predict and explore how fires may spread. The model of fire spread on forest wildfire was established to determine the fire properties. The fire spread model is prepared based on the equation of the diffusion reaction model. There are many methods to estimate the spread of fire. The Kalman Filter Ensemble Method is a modified estimation method of the Kalman Filter algorithm that can be used to estimate linear and non-linear system models. In this research will apply Ensemble Kalman Filter (EnKF) method to estimate the spread of fire on forest wildfire. Before applying the EnKF method, the fire spread model will be discreted using finite difference method. At the end, the analysis obtained illustrated by numerical simulation using software. The simulation results show that the Ensemble Kalman Filter method is closer to the system model when the ensemble value is greater, while the covariance value of the system model and the smaller the measurement.
Series load induction heating inverter state estimator using Kalman filter
Directory of Open Access Journals (Sweden)
Szelitzky T.
2011-12-01
Full Text Available LQR and H2 controllers require access to the states of the controlled system. The method based on description function with Fourier series results in a model with immeasurable states. For this reason, we proposed a Kalman filter based state estimator, which not only filters the input signals, but also computes the unobservable states of the system. The algorithm of the filter was implemented in LabVIEW v8.6 and tested on recorded data obtained from a 10-40 kHz series load frequency controlled induction heating inverter.
Observation Quality Control with a Robust Ensemble Kalman Filter
Roh, Soojin
2013-12-01
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Research on Kalman-filter based multisensor data fusion
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc.Various multisensor data fusion methods have been extensively investigated by researchers,of which Klaman filtering is one of the most important.Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown.states of a dynamic system,which has found widespread application in many areas.The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods.then a new method of state fusion is proposed.Finally the simulation results demonstrate the effectiveness of the introduced method.
Observation Quality Control with a Robust Ensemble Kalman Filter
Roh, Soojin; Genton, Marc G.; Jun, Mikyoung; Szunyogh, Istvan; Hoteit, Ibrahim
2013-01-01
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Nonlinear Kalman filtering in affine term structure models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris
2014-01-01
The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...... performs well when compared with the much more computationally intensive particle filter. These findings suggest that the unscented Kalman filter may be a good approach for a variety of problems in fixed-income pricing....
Kalman Filtered MR Temperature Imaging for Laser Induced Thermal Therapies
Fuentes, D.; Yung, J.; Hazle, J. D.; Weinberg, J. S.; Stafford, R. J.
2011-01-01
The feasibility of using a stochastic form of Pennes bioheat model within a 3D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comp...
Parallel Kalman filter track fit based on vector classes
Energy Technology Data Exchange (ETDEWEB)
Kisel, Ivan [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH (Germany); Kretz, Matthias [Kirchhoff-Institut fuer Physik, Ruprecht-Karls Universitaet, Heidelberg (Germany); Kulakov, Igor [Goethe-Universitaet, Frankfurt am Main (Germany); National Taras Shevchenko University, Kyiv (Ukraine)
2010-07-01
Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman filter. Therefore, developing the fast Kalman filter algorithm, which uses maximum available power of modern processors, is important, in particular for initial selection of events interesting for the new physics. One of processors features, which can speed up the algorithm, is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. Therefore a flexible and useful interface, which uses the SIMD instruction set on different CPU and GPU processors architectures, has been realized as a vector classes library. The Kalman filter based track fitting algorithm has been implemented with use of the vector classes. Fitting quality tests show good results with the residuals equal to 49 {mu}m and 44 {mu}m for x and y track parameters and relative momentum resolution of 0.7%. The fitting time of 0.053 {mu}s per track has been achieved on Intel Xeon X5550 with 8 cores at 2.6 GHz by using in addition Intel Threading Building Blocks.
Kelly, D. A.; Fermelia, A.; Lee, G. K. F.
1990-01-01
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.
Neutron flux filtration using Kalman filter
International Nuclear Information System (INIS)
Urcikan, Marian
2005-01-01
In the course of the WWER-440 start-up procedure the time dependent reactivity is determined from the measured ionization chamber signal by inverse kinetic method. Due to the random nature of the fission process and random nature the detection process the measured ionization chamber signal contains certain noise content. To minimize the unwonted noise on measured reactivity one of the possibility method is utilization Kalman filter, based on a stochastic model of reactor system (Author)
Multivariate localization methods for ensemble Kalman filtering
S. Roh; M. Jun; I. Szunyogh; M. G. Genton
2015-01-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of ...
Recursive B-spline approximation using the Kalman filter
Directory of Open Access Journals (Sweden)
Jens Jauch
2017-02-01
Full Text Available This paper proposes a novel recursive B-spline approximation (RBA algorithm which approximates an unbounded number of data points with a B-spline function and achieves lower computational effort compared with previous algorithms. Conventional recursive algorithms based on the Kalman filter (KF restrict the approximation to a bounded and predefined interval. Conversely RBA includes a novel shift operation that enables to shift estimated B-spline coefficients in the state vector of a KF. This allows to adapt the interval in which the B-spline function can approximate data points during run-time.
Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems
Directory of Open Access Journals (Sweden)
Min Chul Kim
2011-10-01
Full Text Available Recently, the range of available Radio Frequency Identification (RFID tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.
Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.
Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon
2011-01-01
Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.
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.
Kalman Filtering for Delayed Singular Systems with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang
2016-01-01
Kalman filtering problem for singular systems is dealt with, where the measurements consist of instantaneous measurements and delayed ones, and the plant includes multiplicative noise. By utilizing standard singular value decomposition, the restricted equivalent delayed system is presented, and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma. The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations, and a numerical example is presented to show the validity and efficiency of the proposed approach, where the comparison between the filter and predictor is also given.
A Kalman Filtering Perspective for Multiatlas Segmentation*
Gao, Yi; Zhu, Liangjia; Cates, Joshua; MacLeod, Rob S.; Bouix, Sylvain; Tannenbaum, Allen
2016-01-01
In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity—neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy. PMID:26807162
Adaptive Kalman filtering for diagnosis of multiple component degradations
International Nuclear Information System (INIS)
Aumeier, S. E.; Alpay, B.; Lee, J. C.
2005-01-01
We have developed an adaptive Kalman filtering algorithm for the diagnosis of faults or degradations of multiple components in nuclear power plants. We propose to detect the presence and magnitude of the fault(s) through noisy system observations when the measurements indicate significant deviations from predictions. Our diagnostic algorithm uses the measurement residuals, i.e., the difference between the measurements and predictions, to generate a noise input to the uncertain component state in an adaptive Kalman filtering algorithm so that various postulated component transitions or degradations may be statistically represented. The diagnostic algorithm has been tested with a balance of plant (BOP) model of a boiling water reactor (BWR). We have presented a set of algorithms for the detection and diagnosis of component faults of arbitrary magnitude and type within a multi-component system. By analyzing a number of transients including the one example illustrated in the paper, we find that these algorithms are not only capable of determining the correct component fault and magnitude for single components but also they can be used to determine binary faults satisfactorily. Additional study is under way to evaluate the performance of the proposed algorithm including the sensitivity of the diagnostic time to adaptive noise matrix introduced (see equations 7 and 8 illustrated in the paper)
Kalman filtered MR temperature imaging for laser induced thermal therapies.
Fuentes, D; Yung, J; Hazle, J D; Weinberg, J S; Stafford, R J
2012-04-01
The feasibility of using a stochastic form of Pennes bioheat model within a 3-D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L(2) (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error 10 sec.
Deterministic Mean-Field Ensemble Kalman Filtering
Law, Kody
2016-05-03
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
Deterministic Mean-Field Ensemble Kalman Filtering
Law, Kody; Tembine, Hamidou; Tempone, Raul
2016-01-01
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms
Aman, Beshir M.
2012-12-01
This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.
Fundamental aspects of the Kalman filter with examples regarding load forecasting and acid rain
Energy Technology Data Exchange (ETDEWEB)
Molenaar, J.; Visser, H.
1989-02-01
Time-series analysis has become an important tool in research fields such as econometrics, medicine, environmental sciences etc. The Kalman filter is a powerful algorithm for estimation of a wide variety of time-series models. A detailed derivation of the Kalman filter formulae is presented in this contribution. It is also shown how a class of time-series models, the so-called structural models, can be estimated by the Kalman filter. Two examples related to electricity generation are described. 5 figs., 22 refs.
Filtering Meteoroid Flights Using Multiple Unscented Kalman Filters
Sansom, E. K.; Bland, P. A.; Rutten, M. G.; Paxman, J.; Towner, M. C.
2016-11-01
Estimator algorithms are immensely versatile and powerful tools that can be applied to any problem where a dynamic system can be modeled by a set of equations and where observations are available. A well designed estimator enables system states to be optimally predicted and errors to be rigorously quantified. Unscented Kalman filters (UKFs) and interactive multiple models can be found in methods from satellite tracking to self-driving cars. The luminous trajectory of the Bunburra Rockhole fireball was observed by the Desert Fireball Network in mid-2007. The recorded data set is used in this paper to examine the application of these two techniques as a viable approach to characterizing fireball dynamics. The nonlinear, single-body system of equations, used to model meteoroid entry through the atmosphere, is challenged by gross fragmentation events that may occur. The incorporation of the UKF within an interactive multiple model smoother provides a likely solution for when fragmentation events may occur as well as providing a statistical analysis of the state uncertainties. In addition to these benefits, another advantage of this approach is its automatability for use within an image processing pipeline to facilitate large fireball data analyses and meteorite recoveries.
Fractional kalman filter to estimate the concentration of air pollution
Vita Oktaviana, Yessy; Apriliani, Erna; Khusnul Arif, Didik
2018-04-01
Air pollution problem gives important effect in quality environment and quality of human’s life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution’s problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).
IAE-adaptive Kalman filter for INS/GPS integrated navigation system
Institute of Scientific and Technical Information of China (English)
Bian Hongwei; Jin Zhihua; Tian Weifeng
2006-01-01
A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics
Institute of Scientific and Technical Information of China (English)
Zhaoxia PU; Joshua HACKER
2009-01-01
This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.
Accounting for model error due to unresolved scales within ensemble Kalman filtering
Mitchell, Lewis; Carrassi, Alberto
2014-01-01
We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are describe...
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.
Estimation of three-dimensional radar tracking using modified extended kalman filter
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%.
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
Directory of Open Access Journals (Sweden)
Xi Liu
2016-09-01
Full Text Available A new algorithm called maximum correntropy unscented Kalman filter (MCUKF is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC, the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.
Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter
Directory of Open Access Journals (Sweden)
Jing Li
2016-01-01
Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.
Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations
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Romy Budhi Widodo
2016-01-01
Full Text Available Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1 the KF-based model without compensating for external acceleration, (2 the proposed KF-based model which employs the external acceleration compensation model, (3 the two-step KF using weighted-based switching approach, (4 the KF-based model which uses the threshold-based approach, and (5 the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.
On tempo tracking: Tempogram representation and Kalman filtering
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
An iterative ensemble Kalman filter for reservoir engineering applications
Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.
2009-01-01
The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the
Improving Artificial eural etwork Forecasts with Kalman Filtering
African Journals Online (AJOL)
Nafiisah
technique in financial time series and the application of a Kalman filter ... networks (ANN) model using a Kalman filter leads to significant improvements in .... 3-rd order polynomial (Galanis et al. (2006)): 1 t p. 2 t p. 3 t p. 4 t p. 1 t h. 2 t h tr t r ...
Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Shiyuan Wang
2017-01-01
Full Text Available Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.
Soja, B.; Krasna, H.; Boehm, J.; Gross, R. S.; Abbondanza, C.; Chin, T. M.; Heflin, M. B.; Parker, J. W.; Wu, X.
2017-12-01
The most recent realizations of the ITRS include several innovations, two of which are especially relevant to this study. On the one hand, the IERS ITRS combination center at DGFI-TUM introduced a two-level approach with DTRF2014, consisting of a classical deterministic frame based on normal equations and an optional coordinate time series of non-tidal displacements calculated from geophysical loading models. On the other hand, the JTRF2014 by the combination center at JPL is a time series representation of the ITRF determined by Kalman filtering. Both the JTRF2014 and the second level of the DTRF2014 are thus able to take into account short-term variations in the station coordinates. In this study, based on VLBI data, we combine these two approaches, applying them to the determination of both terrestrial and celestial reference frames. Our product has two levels like DTRF2014, with the second level being a Kalman filter solution like JTRF2014. First, we compute a classical TRF and CRF in a global least-squares adjustment by stacking normal equations from 5446 VLBI sessions between 1979 and 2016 using the Vienna VLBI and Satellite Software VieVS (solution level 1). Next, we obtain coordinate residuals from the global adjustment by applying the level-1 TRF and CRF in the single-session analysis and estimating coordinate offsets. These residuals are fed into a Kalman filter and smoother, taking into account the stochastic properties of the individual stations and radio sources. The resulting coordinate time series (solution level 2) serve as an additional layer representing irregular variations not considered in the first level of our approach. Both levels of our solution are implemented in VieVS in order to test their individual and combined performance regarding the repeatabilities of estimated baseline lengths, EOP, and radio source coordinates.
Directory of Open Access Journals (Sweden)
Haoqian Huang
2014-12-01
Full Text Available High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF based on the quaternion expanded to the state variable (BD-AEKF. The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-12-03
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
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.
Wang, Rui; Li, Yanxiao; Sun, Hui; Chen, Zengqiang
2017-11-01
The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Kalman Filter for Spinning Spacecraft Attitude Estimation
Markley, F. Landis; Sedlak, Joseph E.
2008-01-01
This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames by employing a reduced six-component error state. Four variants of the filter, defined by different choices for the reduced error state, are tested against a quaternion-based filter using simulated data for the THEMIS mission. Three of these variants choose three of the components of the error state to be the infinitesimal attitude error angles, facilitating the computation of measurement sensitivity matrices and causing the usual 3x3 attitude covariance matrix to be a submatrix of the 6x6 covariance of the error state. These variants differ in their choice for the other three components of the error state. The variant employing the infinitesimal attitude error angles and the angular momentum components in an inertial reference frame as the error state shows the best combination of robustness and efficiency in the simulations. Attitude estimation results using THEMIS flight data are also presented.
Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter
Institute of Scientific and Technical Information of China (English)
YANG Bin-hu; YANG Wei-dong; CHEN Lian-gui; QU Lei
2008-01-01
Automatic gauge control is an essentially nonlinear process varying with time delay, and stochastically varying input and process noise always influence the target gauge control accuracy. To improve the control capability of feedforward automatic gauge control, Kalman filter was employed to filter the noise signal transferred from one stand to another. The linearized matrix that the Kalman filter algorithm needed was concluded; thus, the feedforward automatic gauge control architecture was dynamically optimized. The theoretical analyses and simulation show that the proposed algorithm is reasonable and effective.
International Nuclear Information System (INIS)
Tamboli, P.K.; Duttagupta, Siddhartha P.; Roy, Kallol
2016-01-01
Highlights: • Derivation for delay compensation algorithm using recursive Kalman filter. • Derivation for delay compensation algorithm using Linear Matrix Inequality based H infinity filter. • Process modeling suitable for delay compensation. • Dynamic tuning of the delay compensation algorithm for both Kalman and H infinity filter. • Simulations and trade-off curve for Kalman and H infinity filter. - Abstract: This paper deals with delay compensation of vanadium Self Powered Neutron Detectors (SPNDs) using Linear Matrix Inequality (LMI) based H-infinity filtering method and compares the results with Kalman filtering method. The entire study is established upon the framework of neutron flux estimation in large core Pressurized Heavy Water Reactor (PHWR) in which delayed SPNDs such as vanadium SPNDs are used as in-core flux monitoring detectors. The use of vanadium SPNDs are limited to 3-D flux mapping despite of providing better Signal to Noise Ratio as compared to other prompt SPNDs, due to their small prompt component in the signal. The use of an appropriate delay compensation technique has been always considered to be an effective strategy to build a prompt and accurate estimate of the neutron flux. We also indicate the noise-response trade-off curve for both the techniques. Since all the delay compensation algorithms always suffer from noise amplification, we propose an efficient adaptive parameter tuning technique for improving performance of the filtering algorithm against noise in the measurement.
International Nuclear Information System (INIS)
Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang
2011-01-01
An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.
Performance Analysis of Local Ensemble Kalman Filter
Tong, Xin T.
2018-03-01
Ensemble Kalman filter (EnKF) is an important data assimilation method for high-dimensional geophysical systems. Efficient implementation of EnKF in practice often involves the localization technique, which updates each component using only information within a local radius. This paper rigorously analyzes the local EnKF (LEnKF) for linear systems and shows that the filter error can be dominated by the ensemble covariance, as long as (1) the sample size exceeds the logarithmic of state dimension and a constant that depends only on the local radius; (2) the forecast covariance matrix admits a stable localized structure. In particular, this indicates that with small system and observation noises, the filter error will be accurate in long time even if the initialization is not. The analysis also reveals an intrinsic inconsistency caused by the localization technique, and a stable localized structure is necessary to control this inconsistency. While this structure is usually taken for granted for the operation of LEnKF, it can also be rigorously proved for linear systems with sparse local observations and weak local interactions. These theoretical results are also validated by numerical implementation of LEnKF on a simple stochastic turbulence in two dynamical regimes.
Kalman filtering for rhodium self-powered neutron detectors
International Nuclear Information System (INIS)
Kantrowitz, M.L.
1988-01-01
Rhodium self-powered neutron detectors are utilized in many pressurized water reactors to determine the neutronic behavior within the core. In order to compensate for the inherent time delay associated with the response of these detectors, a dynamic compensation algorithm is currently used in Combustion Engineering plants to reconstruct the dynamic flux signal which is being sensed by the rhodium detectors. This paper describes a new dynamic compensation algorithm, based on Kalman filtering, which improves on the noise gain and response time characteristics of the algorithm currently used, and offers the possibility of utilizing the proven rhodium detector based fixed in-core detector system as an integral part of advanced core control and/or protection systems
Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J
2014-05-01
In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter
International Nuclear Information System (INIS)
Huang Jin-Wang; Feng Jiu-Chao
2014-01-01
For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)
Enhancement of kalman filter single loss detection capability
International Nuclear Information System (INIS)
Morrison, G.W.; Downing, D.J.; Pike, D.H.
1980-01-01
A new technique to significantly increase the sensitivity of the Kalman filter to detect one-time losses for nuclear marterial accountability and control has been developed. The technique uses the innovations sequence obtained from a Kalman filter analysis of a material balance area. The innovations are distributed as zero mean independent Gaussion random variables with known variance. This property enables an estimator to be formed with enhanced one time loss detection capabilities. Simulation studies of a material balance area indicate the new estimator greatly enhances the one time loss detection capability of the Kalman filter
Directory of Open Access Journals (Sweden)
Erna Apriliani
2011-01-01
Full Text Available Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble estimation by using the singular value decomposition (the SVD, and then we reduced the rank of the diagonal matrix of those singular values. We make a simulation by using Matlab program. We took some the number of ensemble such as 100, 200 and 500. We compared the computational time and the accuracy between the square root ensemble Kalman filter and the ensemble Kalman filter. The reduced rank ensemble Kalman filter can’t be applied in this problem because the dimension of state variable is too less.
Kalman Filtering for Delayed Singular Systems with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang
2016-01-01
Kalman filtering problem for singular systems is dealt with,where the measurements consist of instantaneous measurements and delayed ones,and the plant includes multiplicative noise.By utilizing standard singular value decomposition,the restricted equivalent delayed system is presented,and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma.The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations,and a numerical example is presented to show the validity and efficiency of the proposed approach,where the comparison between the filter and predictor is also given.
The discrete Kalman filtering approach for seismic signals deconvolution
International Nuclear Information System (INIS)
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-01-01
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
Longitudinal Factor Score Estimation Using the Kalman Filter.
Oud, Johan H.; And Others
1990-01-01
How longitudinal factor score estimation--the estimation of the evolution of factor scores for individual examinees over time--can profit from the Kalman filter technique is described. The Kalman estimates change more cautiously over time, have lower estimation error variances, and reproduce the LISREL program latent state correlations more…
Ensemble Kalman filtering with residual nudging
Luo, X.
2012-10-03
Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
Autonomous mobile robot localization using Kalman filter
Directory of Open Access Journals (Sweden)
Mohd Nasir Nabil Zhafri
2017-01-01
Full Text Available Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly affected by the inaccuracy of the sensors. These inaccuracies are caused by various factors which includes internal interferences of the sensor and external environment noises. In order to overcome these noises, a filtering method is required in order to improve the mobile robot’s localization. In this research, a 2- wheeled-drive (2WD mobile robot will be used as platform. The odometers, inertial measurement unit (IMU, and ultrasonic sensors are used for data collection. Data collected is processed using Kalman filter to predict and correct the error from these sensors reading. The differential drive model and measurement model which estimates the environmental noises and predict a correction are used in this research. Based on the simulation and experimental results, the x, y and heading was corrected by converging the error to10 mm, 10 mm and 0.06 rad respectively.
Ensemble Kalman filtering with residual nudging
Directory of Open Access Journals (Sweden)
Xiaodong Luo
2012-10-01
Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
A Kalman-filter estimate of the tidal harmonic constants
International Nuclear Information System (INIS)
Morsetti, R.
1983-01-01
A Kalman-filter estimate of the tidal harmonic constants is proposed in order to take into account their stochastic behaviour. The filter algorithm has been applied to a state-space model of a stochastic system in which the state is defined by the harmonic constants themselves. The results, analysing Trieste sea-level data, have demonstrated that this approach is very suitable for such a purpose, since good estimates and excellent resolution capabilities have been obtained. Furthermore, this method can be very useful also from a practical point of view because real-time computation of the harmonic constants can be developed where an opportune sea-level data acquisition system is available. In conclusion, this paper has emphasized that tidal harmonic constants have to be treated like random variables and, in consequence, new method of analysis can be used
Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.
Kelly, David; Majda, Andrew J; Tong, Xin T
2015-08-25
The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.
An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.
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.
Unscented Kalman filtering in the additive noise case
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The unscented Kalman filter(UKF) has four implementations in the additive noise case,according to whether the state is augmented with noise vectors and whether a new set of sigma points is redrawn from the predicted state(which is so-called resampling) for the observation prediction.This paper concerns the differences of performances for those implementations,such as accuracy,adaptability,computational complexity,etc.The conditionally equivalent relationships between the augmented and non-augmented unscented transforms(UTs) are proved for several sampling strategies that are commonly used.Then,we find that the augmented and non-augmented UKFs have the same filter results with the additive measurement noise,but only have the same state predictions with the additive process noise.Resampling is not believed to be necessary in some researches.However,we find out that resampling can be helpful for an adaptive Kalman gain.This will improve the convergence and accuracy of the filter when the large scale state modeling bias or unknown maneuvers occur.Finally,some universal designing principles for a practical UKF are given as follows:1) for the additive observation noise case,it’s better to use the non-augmented UKF;2) for the additive process noise case,when the small state modeling bias or maneuvers are involved,the non-resampling algorithms with state whether augmented or not are candidates for filters;3) the resampling and non-augmented algorithm is the only choice while the large state modeling bias or maneuvers are latent.
Orchard navigation using derivative free Kalman filtering
DEFF Research Database (Denmark)
Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian
2011-01-01
This paper describes the use of derivative free filters for mobile robot localization and navigation in an orchard. The localization algorithm fuses odometry and gyro measurements with line features representing the surrounding fruit trees of the orchard. The line features are created on basis of 2...
Solving Assembly Sequence Planning using Angle Modulated Simulated Kalman Filter
Mustapa, Ainizar; Yusof, Zulkifli Md.; Adam, Asrul; Muhammad, Badaruddin; Ibrahim, Zuwairie
2018-03-01
This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. Next, the Angle Modulated SKF (AMSKF) is proposed for solving ASP problem. The main idea of the angle modulated approach in solving combinatorial optimization problem is to use a function, g(x), to create a continuous signal. The performance of the proposed AMSKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that AMSKF outperformed all the algorithms in obtaining the best solution.
Attitude Estimation Based on the Spherical Simplex Transformation Modified Unscented Kalman Filter
Directory of Open Access Journals (Sweden)
Jianwei Zhao
2014-01-01
Full Text Available An antenna attitude estimation algorithm is proposed to improve the antenna pointing accuracy for the satellite communication on-the-move. The extrapolated angular acceleration is adopted to improve the performance of the time response. The states of the system are modified according to the modification rules. The spherical simplex transformation unscented Kalman filter is used to improve the precision of the estimated attitude and decrease the calculation of the unscented Kalman filter. The experiment results show that the proposed algorithm can improve the instantaneity of the estimated attitude and the precision of the antenna pointing, which meets the requirement of the antenna pointing.
Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations
Directory of Open Access Journals (Sweden)
Pengpeng Chen
2015-01-01
Full Text Available This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs. Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.
Proofs and Techniques Useful for Deriving the Kalman Filter
National Research Council Canada - National Science Library
Koks, Don
2008-01-01
This note is a tutorial in matrix manipulation and the normal distribution of statistics, concepts that are important for deriving and analysing the Kalman Filter, a basic tool of signal processing...
Kalman filter data assimilation: targeting observations and parameter estimation.
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
A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation
Altaf, Muhammad
2014-08-01
This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.
A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation
Altaf, Muhammad; Butler, T.; Mayo, T.; Luo, X.; Dawson, C.; Heemink, A. W.; Hoteit, Ibrahim
2014-01-01
This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.
BWR level estimation using Kalman Filtering approach
International Nuclear Information System (INIS)
Garner, G.; Divakaruni, S.M.; Meyer, J.E.
1986-01-01
Work is in progress on development of a system for Boiling Water Reactor (BWR) vessel level validation and failure detection. The levels validated include the liquid level both inside and outside the core shroud. This work is a major part of a larger effort to develop a complete system for BWR signal validation. The demonstration plant is the Oyster Creek BWR. Liquid level inside the core shroud is not directly measured during full power operation. This level must be validated using measurements of other quantities and analytic models. Given the available sensors, analytic models for level that are based on mass and energy balances can contain open integrators. When such a model is driven by noisy measurements, the model predicted level will deviate from the true level over time. To validate the level properly and to avoid false alarms, the open integrator must be stabilized. In addition, plant parameters will change slowly with time. The respective model must either account for these plant changes or be insensitive to them to avoid false alarms and maintain sensitivity to true failures of level instrumentation. Problems are addressed here by combining the extended Kalman Filter and Parity Space Decision/Estimator. The open integrator is stabilized by integrating from the validated estimate at the beginning of each sampling interval, rather than from the model predicted value. The model is adapted to slow plant/sensor changes by updating model parameters on-line
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-12-03
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-05-08
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.
2015-12-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.
2015-01-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Motion estimation using point cluster method and Kalman filter.
Senesh, M; Wolf, A
2009-05-01
The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal
Kalman-Takens filtering in the presence of dynamical noise
Hamilton, Franz; Berry, Tyrus; Sauer, Timothy
2017-12-01
The use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been developed for this purpose. If no model is known, a hybrid Kalman-Takens method has been recently introduced, in order to exploit the advantages of optimal filtering in a nonparametric setting. This procedure replaces the parametric model with dynamics reconstructed from delay coordinates, while using the Kalman update formulation to assimilate new observations. In this article, we study the efficacy of this method for identifying underlying dynamics in the presence of dynamical noise. Furthermore, by combining the Kalman-Takens method with an adaptive filtering procedure we are able to estimate the statistics of the observational and dynamical noise. This solves a long-standing problem of separating dynamical and observational noise in time series data, which is especially challenging when no dynamical model is specified.
A Brief Tutorial on the Ensemble Kalman Filter
Mandel, Jan
2009-01-01
The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the s...
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.
Kalman filtering of self-powered neutron detectors
International Nuclear Information System (INIS)
Kantrowitz, M.L.
1992-01-01
Pressurized water reactors employ a wide variety of in-core detectors to determine the neutronic behavior within the core. Among the detectors used are rhodium and vanadium self-powered detectors (SPDs), which are very accurate, but respond slowly to changes in neutron flux. This paper describes a new dynamic compensation algorithm, based on Kalman filtering, which converts delayed-responding rhodium and vanadium SPDs into prompt-responding detectors by reconstructing the dynamic flux signal sensed by the detectors from the prompt and delayed components. This conversion offers the possibility of utilizing current fixed in-core detector systems based on these delayed-responding detectors for core control and/or core protection functions without the need for fixed in-core detectors which are prompt-responding. As a result, the capabilities of current fixed in-core detector systems could be expanded significantly without a major hardware investment
Switching Kalman filter for failure prognostic
Lim, Chi Keong Reuben; Mba, David
2015-02-01
The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.
Efficient decoding with steady-state Kalman filter in neural interface systems.
Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R
2011-02-01
The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.
Kalman Filter for Calibrating a Telescope Focal Plane
Kang, Bryan; Bayard, David
2006-01-01
The instrument-pointing frame (IPF) Kalman filter, and an algorithm that implements this filter, have been devised for calibrating the focal plane of a telescope. As used here, calibration signifies, more specifically, a combination of measurements and calculations directed toward ensuring accuracy in aiming the telescope and determining the locations of objects imaged in various arrays of photodetectors in instruments located on the focal plane. The IPF Kalman filter was originally intended for application to a spaceborne infrared astronomical telescope, but can also be applied to other spaceborne and ground-based telescopes. In the traditional approach to calibration of a telescope, (1) one team of experts concentrates on estimating parameters (e.g., pointing alignments and gyroscope drifts) that are classified as being of primarily an engineering nature, (2) another team of experts concentrates on estimating calibration parameters (e.g., plate scales and optical distortions) that are classified as being primarily of a scientific nature, and (3) the two teams repeatedly exchange data in an iterative process in which each team refines its estimates with the help of the data provided by the other team. This iterative process is inefficient and uneconomical because it is time-consuming and entails the maintenance of two survey teams and the development of computer programs specific to the requirements of each team. Moreover, theoretical analysis reveals that the engineering/ science iterative approach is not optimal in that it does not yield the best estimates of focal-plane parameters and, depending on the application, may not even enable convergence toward a set of estimates.
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.
Kalman filter for speech enhancement in cocktail party scenarios using a codebook-based approach
DEFF Research Database (Denmark)
Kavalekalam, Mathew Shaji; Christensen, Mads Græsbøll; Gran, Fredrik
2016-01-01
Enhancement of speech in non-stationary background noise is a challenging task, and conventional single channel speech enhancement algorithms have not been able to improve the speech intelligibility in such scenarios. The work proposed in this paper investigates a single channel Kalman filter based...... trained codebook over a generic speech codebook in relation to the performance of the speech enhancement system....
A Quantised State Systems Approach for Jacobian Free Extended Kalman Filtering
DEFF Research Database (Denmark)
Alminde, Lars; Bendtsen, Jan Dimon; Stoustrup, Jakob
2007-01-01
Model based methods for control of intelligent autonomous systems rely on a state estimate being available. One of the most common methods to obtain a state estimate for non-linear systems is the Extended Kalman Filter (EKF) algorithm. In order to apply the EKF an expression must be available...
Modeling of HVDC in Dynamic State Estimation Using Unscented Kalman Filter Method
DEFF Research Database (Denmark)
Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth
2016-01-01
HVDC transmission is an integral part of various power system networks. This article presents an Unscented Kalman Filter dynamic state estimator algorithm that considers the presence of HVDC links. The AC - DC power flow analysis, which is implemented as power flow solver for Dynamic State...
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Bizhong Xia
2015-06-01
Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.
Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel
2016-10-31
Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.
Directory of Open Access Journals (Sweden)
Segundo Esteban
2016-10-01
Full Text Available Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.
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).
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.
Kalman filtering applied to a reagent feed system
International Nuclear Information System (INIS)
Griffin, C.D.; Croson, D.V.; Feeley, J.J.
1988-01-01
Using a Kalman filter solves a troublesome measurement noise problem and, at the same time, improves nuclear safety by detecting leaks to the process' feed tanks. To demonstrate how this technology of optimal estimation can be exploited, this article presents a systematic plan and example of how a Kalman filter was proven in industrial use on a reagent analyzer. A process to recycle uranium from spent fuel elements uses a reagent stream containing boron to dissolve the fuel. The boron is the neutron poison that prevents a nuclear chain reaction during the uranium dissolution. The purpose of the Kalman filter for this system is to reduce the uncertainty in the boron concentration measurement. The filter also provides incipient fault detection by estimating the unmeasured state of any unpoisoned solution, which would dilute the boron solution, entering the feed vessel
Applications of Kalman filters based on non-linear functions to numerical weather predictions
Directory of Open Access Journals (Sweden)
G. Galanis
2006-10-01
Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.
Applications of Kalman filters based on non-linear functions to numerical weather predictions
Directory of Open Access Journals (Sweden)
G. Galanis
2006-10-01
Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.
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.
A Kalman filter technique applied for medical image reconstruction
International Nuclear Information System (INIS)
Goliaei, S.; Ghorshi, S.; Manzuri, M. T.; Mortazavi, M.
2011-01-01
Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Image reconstruction is essential for medical images for some applications such as suppression of noise or de-blurring the image in order to provide images with better quality and contrast. Due to vital rule of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is desirable. Most algorithms in image reconstruction are operated on frequency domain such as the most popular one known as filtered back projection. In this paper we introduce a Kalman filter technique which is operated in time domain for medical image reconstruction. Results indicated that as the number of projection increases in both normal collected ray sum and the collected ray sum corrupted by noise the quality of reconstructed image becomes better in terms of contract and transparency. It is also seen that as the number of projection increases the error index decreases.
Comparison of several Kalman filter models for establishing MUF
International Nuclear Information System (INIS)
Pike, D.H.; Morrison, G.W.; Holland, C.W.
1976-01-01
Detection of MUF in a material balance area is a problem in nuclear material control. It has been shown that the Kalman filter can detect a MUF in situations which could not be detected by the traditional control chart approach using LEMUF. The Kalman filter is extended in this paper to cover two additional scenarios: (1) the case where a random quantity with a mean of M(t) is removed per period, and (2) the case where MUF is a fraction of the on-hand inventory each period. The Kalman filter is robust, sensitive, produces estimates of the error covariance matrix, and is an iterative technique which is suited for on-line-direct-input information systems
Nonlinear dynamical system identification using unscented Kalman filter
Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan
2016-11-01
Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.
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.
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-12-01
Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-01-01
Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.
Forecasting with the Standardized Self-Perturbed Kalman Filter
DEFF Research Database (Denmark)
Grassi, Stefano; Nonejad, Nima; Santucci de Magistris, Paolo
We propose and study the finite-sample properties of a modified version of the self-perturbed Kalman filter of Park and Jun (1992) for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is now weighted...... compared to other on-line, classical and Bayesian methods. The standardized self-perturbed Kalman filter is adopted to forecast the equity premium on the S&P500 index under several model specifications, and to investigate to what extent and how realized variance can be exploited to predict excess returns....
Prior knowledge processing for initial state of Kalman filter
Czech Academy of Sciences Publication Activity Database
Suzdaleva, Evgenia
2010-01-01
Roč. 24, č. 3 (2010), s. 188-202 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP201/06/P434 Institutional research plan: CEZ:AV0Z10750506 Keywords : Kalman filtering * prior knowledge * state-space model * initial state distribution Subject RIV: BC - Control Systems Theory Impact factor: 0.729, year: 2010 http://library.utia.cas.cz/separaty/2009/AS/suzdaleva-prior knowledge processing for initial state of kalman filter.pdf
A THEORETICAL STUDY ON SIMPLIFIED KALMAN FILTER IN DATA ASSIMILATION
Institute of Scientific and Technical Information of China (English)
Ma Zhai-pu; Huang Da-ji; Zhang Ben-zhao
2003-01-01
In this paper, we put forward a new method to reduce the calculation amount of the gain matrix of Kalman filter in data assimilation. We rewrite the vector describing the total state variables with two vectors whose dimensions are small and thus obtain the main parts and the trivial parts of the state variables. On the basis of the rewrittten formula, we not only develop a reduced Kalman filter scheme, but also obtain the transition equations about truncation errors, with which the validity of the main parts acting for the total state variables can be evaluated quantitatively. The error transition equations thus offer an indirect testimony to the rationality of the main parts.
Nonlinear Kalman Filtering in Affine Term Structure Models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris
When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi…ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze...... the potential of the unscented Kalman …filter to properly capture nonlinearities. To illustrate the advantages of the unscented Kalman …filter, we analyze the cross section of swap rates, which are relatively simple non-linear instruments, and cap prices, which are highly nonlinear in the states. An extensive...
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-14
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter
Directory of Open Access Journals (Sweden)
Hairong Chu
2017-01-01
Full Text Available In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Kalman Filter for Generalized 2-D Roesser Models
Institute of Scientific and Technical Information of China (English)
SHENG Mei; ZOU Yun
2007-01-01
The design problem of the state filter for the generalized stochastic 2-D Roesser models, which appears when both the state and measurement are simultaneously subjected to the interference from white noise, is discussed. The wellknown Kalman filter design is extended to the generalized 2-D Roesser models. Based on the method of "scanning line by line", the filtering problem of generalized 2-D Roesser models with mode-energy reconstruction is solved. The formula of the optimal filtering, which minimizes the variance of the estimation error of the state vectors, is derived. The validity of the designed filter is verified by the calculation steps and the examples are introduced.
The Unscented Kalman Filter estimates the plasma insulin from glucose measurement.
Eberle, Claudia; Ament, Christoph
2011-01-01
Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergman's Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
Directory of Open Access Journals (Sweden)
Bowen Hou
2017-11-01
Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.
International Nuclear Information System (INIS)
Boulfelfel, D.; Rangayyan, R.M.; Kuduvalli, G.R.; Hahn, L.J.; Kloiber, R.
1994-01-01
The discrete filtered backprojection (DFBP) algorithm used for the reconstruction of single photon emission computed tomography (SPECT) images affects image quality because of the operations of filtering and discretization. The discretization of the filtered backprojection process can cause the modulation transfer function (MTF) of the SPECT imaging system to be anisotropic and nonstationary, especially near the edges of the camera's field of view. The use of shift-invariant restoration techniques fails to restore large images because these techniques do not account for such variations in the MTF. This study presents the application of a two-dimensional (2-D) shift-variant Kalman filter for post-reconstruction restoration of SPECT slices. This filter was applied to SPECT images of a hollow cylinder phantom; a resolution phantom; and a large, truncated cone phantom containing two types of cold spots, a sphere, and a triangular prism. The images were acquired on an ADAC GENESYS camera. A comparison was performed between results obtained by the Kalman filter and those obtained by shift-invariant filters. Quantitative analysis of the restored images performed through measurement of root mean squared errors shows a considerable reduction in error of Kalman-filtered images over images restored using shift-invariant methods
Traditional Tracking with Kalman Filter on Parallel Architectures
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.
Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation
DEFF Research Database (Denmark)
Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik
2017-01-01
The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...
Earth orientation parameters from VLBI determined with a Kalman filter
Directory of Open Access Journals (Sweden)
Maria Karbon
2017-11-01
We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.
Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.
Feng, Xue; Salerno, Michael; Kramer, Christopher M; Meyer, Craig H
2013-05-01
In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction. Copyright © 2012 Wiley Periodicals, Inc.
The Kalman filter for the pedologist's tool kit
Webster, R.; Heuvelink, G.B.M.
2006-01-01
The Kalman filter is a tool designed primarily to estimate the values of the `state¿ of a dynamic system in time. There are two main equations. These are the state equation, which describes the behaviour of the state over time, and the measurement equation, which describes at what times and in what
Predicting Breeding Values in Animals by Kalman Filter
DEFF Research Database (Denmark)
Karacaoren, B; Janss, L L G; Kadarmideen, H N
2012-01-01
The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during...
On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles
Luo, Xiaodong
2010-09-19
The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].
Ensemble Kalman filtering in presence of inequality constraints
van Leeuwen, P. J.
2009-04-01
Kalman filtering is presence of constraints is an active area of research. Based on the Gaussian assumption for the probability-density functions, it looks hard to bring in extra constraints in the formalism. On the other hand, in geophysical systems we often encounter constraints related to e.g. the underlying physics or chemistry, which are violated by the Gaussian assumption. For instance, concentrations are always non-negative, model layers have non-negative thickness, and sea-ice concentration is between 0 and 1. Several methods to bring inequality constraints into the Kalman-filter formalism have been proposed. One of them is probability density function (pdf) truncation, in which the Gaussian mass from the non-allowed part of the variables is just equally distributed over the pdf where the variables are alolwed, as proposed by Shimada et al. 1998. However, a problem with this method is that the probability that e.g. the sea-ice concentration is zero, is zero! The new method proposed here does not have this drawback. It assumes that the probability-density function is a truncated Gaussian, but the truncated mass is not distributed equally over all allowed values of the variables, but put into a delta distribution at the truncation point. This delta distribution can easily be handled with in Bayes theorem, leading to posterior probability density functions that are also truncated Gaussians with delta distributions at the truncation location. In this way a much better representation of the system is obtained, while still keeping most of the benefits of the Kalman-filter formalism. In the full Kalman filter the formalism is prohibitively expensive in large-scale systems, but efficient implementation is possible in ensemble variants of the kalman filter. Applications to low-dimensional systems and large-scale systems will be discussed.
Randomized Filtering Algorithms
DEFF Research Database (Denmark)
Katriel, Irit; Van Hentenryck, Pascal
2008-01-01
of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...
On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles
Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.
2010-01-01
However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].
Kalman Filter for Estimation of Sensor Acceleration Using Six - axis Inertial Sensor
International Nuclear Information System (INIS)
Lee, Jung Keun
2015-01-01
Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors
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.
Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter
Luo, Xiaodong
2011-12-01
A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.
An aperiodic phenomenon of the unscented Kalman filter in filtering noisy chaotic signals
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A non-periodic oscillatory behavior of the unscented Kalman filter (UKF) when used to filter noisy contaminated chaotic signals is reported. We show both theoretically and experimentally that the gain of the UKF may not converge or diverge but oscillate aperiodically. More precisely, when a nonlinear system is periodic, the Kalman gain and error covariance of the UKF converge to zero. However, when the system being considered is chaotic, the Kalman gain either converges to a fixed point with a magnitude larger than zero or oscillates aperiodically.
Sensor failure detection in dynamical systems by Kalman filtering methodology
International Nuclear Information System (INIS)
Ciftcioglu, O.
1991-03-01
Design of a sensor failure detection system by Kalman filtering methodology is described. The method models the process systems in state-space form, the information on each state being provided by relevant sensors present in the process system. Since the measured states are usually subject to noise, the estimation of the states optimally is an essential requirement. To this end the detection system comprises Kalman estimation filters, the number of which is equal to the number of states concerned. The estimated state of a particular signal in each filter is compared with the corresponding measured signal and difference beyond a predetermined bound is identified as failure, the sensor being identified/isolated as faulty. (author). 19 refs.; 8 figs.; 1 tab
An adaptive Kalman filter for speckle reductions in ultrasound images
International Nuclear Information System (INIS)
Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.
1988-01-01
Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction
Weighted ensemble transform Kalman filter for image assimilation
Directory of Open Access Journals (Sweden)
Sebastien Beyou
2013-01-01
Full Text Available This study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF proposed by Papadakis et al. (2010 for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF, incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.
A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU.
Zihajehzadeh, S; Loh, D; Lee, M; Hoskinson, R; Park, E J
2014-01-01
Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.
Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.
Xia, Youshen; Wang, Jun
2015-07-01
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.
An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters
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
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.
Kalman filter implementation for small satellites using constraint GPS data
Wesam, Elmahy M.; Zhang, Xiang; Lu, Zhengliang; Liao, Wenhe
2017-06-01
Due to the increased need for autonomy, an Extended Kalman Filter (EKF) has been designed to autonomously estimate the orbit using GPS data. A propagation step models the satellite dynamics as a two body with J2 (second zonal effect) perturbations being suitable for orbits in altitudes higher than 600 km. An onboard GPS receiver provides continuous measurement inputs. The continuity of measurements decreases the errors of the orbit determination algorithm. Power restrictions are imposed on small satellites in general and nanosatellites in particular. In cubesats, the GPS is forced to be shut down most of the mission’s life time. GPS is turned on when experiments like atmospheric ones are carried out and meter level accuracy for positioning is required. This accuracy can’t be obtained by other autonomous sensors like magnetometer and sun sensor as they provide kilometer level accuracy. Through simulation using Matlab and satellite tool kit (STK) the position accuracy is analyzed after imposing constrained conditions suitable for small satellites and a very tight one suitable for nanosatellite missions.
Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems
Directory of Open Access Journals (Sweden)
Man Hong
2013-01-01
Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.
A cognition-based method to ease the computational load for an extended Kalman filter.
Li, Yanpeng; Li, Xiang; Deng, Bin; Wang, Hongqiang; Qin, Yuliang
2014-12-03
The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.
Bds/gps Integrated Positioning Method Research Based on Nonlinear Kalman Filtering
Ma, Y.; Yuan, W.; Sun, H.
2017-09-01
In order to realize fast and accurate BDS/GPS integrated positioning, it is necessary to overcome the adverse effects of signal attenuation, multipath effect and echo interference to ensure the result of continuous and accurate navigation and positioning. In this paper, pseudo-range positioning is used as the mathematical model. In the stage of data preprocessing, using precise and smooth carrier phase measurement value to promote the rough pseudo-range measurement value without ambiguity. At last, the Extended Kalman Filter(EKF), the Unscented Kalman Filter(UKF) and the Particle Filter(PF) algorithm are applied in the integrated positioning method for higher positioning accuracy. The experimental results show that the positioning accuracy of PF is the highest, and UKF is better than EKF.
Nonlinear Kalman filters for calibration in radio interferometry
Tasse, C.
2014-06-01
The data produced by the new generation of interferometers are affected by a wide variety of partially unknown complex effects such as pointing errors, phased array beams, ionosphere, troposphere, Faraday rotation, or clock drifts. Most algorithms addressing direction-dependent calibration solve for the effective Jones matrices, and cannot constrain the underlying physical quantities of the radio interferometry measurement equation (RIME). A related difficulty is that they lack robustness in the presence of low signal-to-noise ratios, and when solving for moderate to large numbers of parameters they can be subject to ill-conditioning. These effects can have dramatic consequences in the image plane such as source or even thermal noise suppression. The advantage of solvers directly estimating the physical terms appearing in the RIME is that they can potentially reduce the number of free parameters by orders of magnitudes while dramatically increasing the size of usable data, thereby improving conditioning. We present here a new calibration scheme based on a nonlinear version of the Kalman filter that aims at estimating the physical terms appearing in the RIME. We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. Using simulations we show that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly computationally cheap algorithm that we believe to be robust, especially in low signal-to-noise regimes. Potentially, the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that
Application of adaptive Kalman filter in vehicle laser Doppler velocimetry
Fan, Zhe; Sun, Qiao; Du, Lei; Bai, Jie; Liu, Jingyun
2018-03-01
Due to the variation of road conditions and motor characteristics of vehicle, great root-mean-square (rms) error and outliers would be caused. Application of Kalman filter in laser Doppler velocimetry(LDV) is important to improve the velocity measurement accuracy. In this paper, the state-space model is built by using current statistical model. A strategy containing two steps is adopted to make the filter adaptive and robust. First, the acceleration variance is adaptively adjusted by using the difference of predictive observation and measured observation. Second, the outliers would be identified and the measured noise variance would be adjusted according to the orthogonal property of innovation to reduce the impaction of outliers. The laboratory rotating table experiments show that adaptive Kalman filter greatly reduces the rms error from 0.59 cm/s to 0.22 cm/s and has eliminated all the outliers. Road experiments compared with a microwave radar show that the rms error of LDV is 0.0218 m/s, and it proves that the adaptive Kalman filtering is suitable for vehicle speed signal processing.
Pengenal Gerakan dengan Joystick Akselerometer Menggunakan Filter Kalman
Directory of Open Access Journals (Sweden)
Khoirudin Fathoni
2017-12-01
Full Text Available Human Machine Interaction keeps growing and developing, one of development is through gesture recognition that detects acceleration in a movement. This technology has been applied in joystick Wiimote and Wii-nunchuk by Nintendo that is widely used all over the world. Two main challenges in using accelerometer are to eliminate the noise of the sensor and to cancel the detected gravity acceleration when the joystick is tilted. The noise and gravity acceleration may influence the data reading and create error accumulation, respectively. This work proposes an implementation of Kalman Filter and also a simple technique to eliminate the influence of the gravity acceleration as a solution to solve above problems in using accelerometer of Wii-Nunchuk joystick in Board Arduino Mega 2560. The experimental results in motionless position show that the filter can reduce the gravity acceleration. We have to set the initial value of q and R parameters in the estimation of position, speed, and acceleration using Kalman filter. Once R is decided, the change of q will determine Kk gain, and it will locate the poles of the observer that influence the stability and the estimation result. With R=0.00005 and q=1, the poles of Kalman filter are located in the unit circle so that the estimation is stable and appropriate with the data from the sensor and even cancel the noise.
Ground Level Ozone Peak Forecast using Neural Networks and Kalman Filter
Czech Academy of Sciences Publication Activity Database
Pelikán, Emil; Eben, Kryštof; Vondráček, Jiří; Krejčíř, Pavel; Keder, J.
2000-01-01
Roč. 3, č. 2 (2000), s. 3-8 ISSN 1335-339X Grant - others:APPETISE(XE) IST-99-11764; MŽP ČR(CZ) ZZ520/2/97; MŠMT ČR(CZ) VS96008 Institutional research plan: AV0Z1030915 Keywords : ozone forecast * neural classifications * Kalman filter * genetic algorithms * Kohonen maps * Czech Republic Subject RIV: BB - Applied Statistics, Operational Research
Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter
Directory of Open Access Journals (Sweden)
Fikret Caliskan
2014-01-01
Full Text Available An adaptive modified two-stage linear Kalman filtering algorithm is utilized to identify the loss of control effectiveness and the magnitude of low degree of stuck faults in a closed-loop nonlinear B747 aircraft. Control effectiveness factors and stuck magnitudes are used to quantify faults entering control systems through actuators. Pseudorandom excitation inputs are used to help distinguish partial loss and stuck faults. The partial loss and stuck faults in the stabilizer are isolated and identified successfully.
Bar-Itzhack, I. Y.; Deutschmann, J.; Markley, F. L.
1991-01-01
This work introduces, examines and compares several quaternion normalization algorithms, which are shown to be an effective stage in the application of the additive extended Kalman filter to spacecraft attitude determination, which is based on vector measurements. Three new normalization schemes are introduced. They are compared with one another and with the known brute force normalization scheme, and their efficiency is examined. Simulated satellite data are used to demonstate the performance of all four schemes.
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
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.
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%.
Iglesias, Marco; Sawlan, Zaid A; Scavino, Marco; Tempone, Raul; Wood, Christopher
2017-01-01
In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and parameters of linear parabolic partial differential equations
Spacecraft Dynamics Should be Considered in Kalman Filter Attitude Estimation
Yang, Yaguang; Zhou, Zhiqiang
2016-01-01
Kalman filter based spacecraft attitude estimation has been used in some high-profile missions and has been widely discussed in literature. While some models in spacecraft attitude estimation include spacecraft dynamics, most do not. To our best knowledge, there is no comparison on which model is a better choice. In this paper, we discuss the reasons why spacecraft dynamics should be considered in the Kalman filter based spacecraft attitude estimation problem. We also propose a reduced quaternion spacecraft dynamics model which admits additive noise. Geometry of the reduced quaternion model and the additive noise are discussed. This treatment is more elegant in mathematics and easier in computation. We use some simulation example to verify our claims.
Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter
Curvo, M.
2000-01-01
Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional...
Localization of Wheeled Mobile Robot Based on Extended Kalman Filtering
Directory of Open Access Journals (Sweden)
Li Guangxu
2015-01-01
Full Text Available A mobile robot localization method which combines relative positioning with absolute orientation is presented. The code salver and gyroscope are used for relative positioning, and the laser radar is used to detect absolute orientation. In this paper, we established environmental map, multi-sensor information fusion model, sensors and robot motion model. The Extended Kalman Filtering (EKF is adopted as multi-sensor data fusion technology to realize the precise localization of wheeled mobile robot.
Cubature/ Unscented/ Sigma Point Kalman Filtering with Angular Measurement Models
2015-07-06
similarly transformed to work with the Laplace distribution. Cubature formulae for w(x) = 1 over regions of various shapes could be used for evaluating...measurement and process non- linearities, such as the cubature Kalman filter, can perform ex- tremely poorly in many applications involving angular...in the form of the “unscented transform ”) consider just converting such measurements into Cartesian coordinates and feeding the converted measurements
ANALYSIS OF SST IMAGES BY WEIGHTED ENSEMBLE TRANSFORM KALMAN FILTER
Sai , Gorthi; Beyou , Sébastien; Memin , Etienne
2011-01-01
International audience; This paper presents a novel, efficient scheme for the analysis of Sea Surface Temperature (SST) ocean images. We consider the estimation of the velocity fields and vorticity values from a sequence of oceanic images. The contribution of this paper lies in proposing a novel, robust and simple approach based onWeighted Ensemble Transform Kalman filter (WETKF) data assimilation technique for the analysis of real SST images, that may contain coast regions or large areas of ...
Objective judgement by Kalman filtering in the generalized Landsbergian scheme
International Nuclear Information System (INIS)
Lukacs, B.; Racz, A.
1992-08-01
A method is suggested to check if a non-equilibrium thermodynamic description of a system is complete. Exploring Landsberg's idea of the role of third person, a scheme is proposed for treating non-equilibrium systems as well. In order to suppress irrelevant information carried by measurement noise or for very fast phenomena, Kalman filter can act as the objective spectator. The idea is illustrated via a thermodynamic model of non-relativistic heavy ion collisions. (author) 12 refs.; 3 figs
Detection of Harmonic Occurring using Kalman Filtering
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Shoro, Ghulam Mustafa; Imran, Raja Muhammed
2014-01-01
/current characteristic. These harmonics are not to be allowed to grow beyond a certain limit to avoid any grave consequence to the customer’s main supply. Filters can be implemented at the power source or utility location to eliminate these harmonics. In this paper we detect the instance at which these harmonics occur...
Practical feasibility of Kalman filters for the state estimation of lithium-ion batteries
Campestrini, Christian
2018-01-01
This work investigates the feasibility of the Kalman filter for the state estimation of lithium-ion cells and modules under real conditions. Therefore, the dependencies of the cells during ageing are shown and various Kalman filter types are compared. The strongly varying model parameters, as well as the temperature and ageing dependent open circuit voltage, require an empirical adaptation of the inconstant and non-linear filter tuning parameters. The performance of the Kalman filter in a rea...
Kalman filter analysis of delayed neutron nondestructive assay measurements
International Nuclear Information System (INIS)
Aumeier, S. E.
1998-01-01
The ability to nondestructively determine the presence and quantity of fissile and fertile nuclei in various matrices is important in several nuclear applications including international and domestics safeguards, radioactive waste characterization and nuclear facility operations. Material irradiation followed by delayed neutron counting is a well known and useful nondestructive assay technique used to determine the fissile-effective content of assay samples. Previous studies have demonstrated the feasibility of using Kalman filters to unfold individual isotopic contributions to delayed neutron measurements resulting from the assay of mixes of uranium and plutonium isotopes. However, the studies in question used simulated measurement data and idealized parameters. We present the results of the Kalman filter analysis of several measurements of U/Pu mixes taken using Argonne National Laboratory's delayed neutron nondestructive assay device. The results demonstrate the use of Kalman filters as a signal processing tool to determine the fissile and fertile isotopic content of an assay sample from the aggregate delayed neutron response following neutron irradiation
Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem
Man, J.; Li, W.; Zeng, L.; Wu, L.
2015-12-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the Polynomial Chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Applications of Kalman Filtering to nuclear material control
International Nuclear Information System (INIS)
Pike, D.H.; Morrison, G.W.; Westley, G.W.
1977-10-01
The feasibility of using modern state estimation techniques (specifically Kalman Filtering and Linear Smoothing) to detect losses of material from material balance areas is evaluated. It is shown that state estimation techniques are not only feasible but in most situations are superior to existing methods of analysis. The various techniques compared include Kalman Filtering, linear smoothing, standard control charts, and average cumulative summation (CUSUM) charts. Analysis results indicated that the standard control chart is the least effective method for detecting regularly occurring losses. An improvement in the detection capability over the standard control chart can be realized by use of the CUSUM chart. Even more sensitivity in the ability to detect losses can be realized by use of the Kalman Filter and the linear smoother. It was found that the error-covariance matrix can be used to establish limits of error for state estimates. It is shown that state estimation techniques represent a feasible and desirable method of theft detection. The technique is usually more sensitive than the CUSUM chart in detecting losses. One kind of loss which is difficult to detect using state estimation techniques is a single isolated loss. State estimation procedures are predicated on dynamic models and are well-suited for detecting losses which occur regularly over several accounting periods. A single isolated loss does not conform to this basic assumption and is more difficult to detect
Kalman filtering for time-delayed linear systems
Institute of Scientific and Technical Information of China (English)
LU Xiao; WANG Wei
2006-01-01
This paper is to study the linear minimum variance estimation for discrete- time systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous and double time-delayed measurements. It is shown that the derived estimator involves solving three different standard Kalman filtering with the same dimension as the original system. The obtained results form the basis for solving some complicated problems such as H∞ fixed-lag smoothing, preview control, H∞ filtering and control with time delays.
Temperature profile retrievals with extended Kalman-Bucy filters
Ledsham, W. H.; Staelin, D. H.
1979-01-01
The Extended Kalman-Bucy Filter is a powerful technique for estimating non-stationary random parameters in situations where the received signal is a noisy non-linear function of those parameters. A practical causal filter for retrieving atmospheric temperature profiles from radiances observed at a single scan angle by the Scanning Microwave Spectrometer (SCAMS) carried on the Nimbus 6 satellite typically shows approximately a 10-30% reduction in rms error about the mean at almost all levels below 70 mb when compared with a regression inversion.
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering
DEFF Research Database (Denmark)
Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh
2015-01-01
Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...
International Nuclear Information System (INIS)
Li, Yanwen; Wang, Chao; Gong, Jinfeng
2016-01-01
An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. - Highlights: • Recursive least squares method with fuzzy adaptive forgetting factor is presented. • The innovation obeying chi-square distribution is used to identify uncertainty. • A combination Karman filter approach for State of Charge estimation is presented. • The performance of the proposed method is verified by comparison results.
Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter
Directory of Open Access Journals (Sweden)
Liang Li
2014-01-01
Full Text Available Real-time functional magnetic resonance imaging (rt-fMRI is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.
Kinematic Localization for Global Navigation Satellite Systems: A Kalman Filtering Approach
Tabatabaee, Mohammad Hadi
Use of the Global Positioning System (GNSS) has expanded significantly in the past decade, especially with advances in embedded systems and the emergence of smartphones and the Internet of Things (IoT). The growing demand has stimulated research on development of GNSS techniques and programming tools. The focus of much of the research efforts have been on high-level algorithms and augmentations. This dissertation focuses on the low-level methods at the heart of GNSS systems and proposes a new methods for GNSS positioning problems based on concepts of distance geometry and the use of Kalman filters. The methods presented in this dissertation provide algebraic solutions to problems that have predominantly been solved using iterative methods. The proposed methods are highly efficient, provide accurate estimates, and exhibit a degree of robustness in the presence of unfavorable satellite geometry. The algorithm operates in two stages; an estimation of the receiver clock bias and removal of the bias from the pseudorange observables, followed by the localization of the GNSS receiver. The use of a Kalman filter in between the two stages allows for an improvement of the clock bias estimate with a noticeable impact on the position estimates. The receiver localization step has also been formulated in a linear manner allowing for the direct application of a Kalman filter without any need for linearization. The methodology has also been extended to double differential observables for high accuracy pseudorange and carrier phase position estimates.
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.
Schmidt-Kalman Filter with Polynomial Chaos Expansion for Orbit Determination of Space Objects
Yang, Y.; Cai, H.; Zhang, K.
2016-09-01
Parameter errors in orbital models can result in poor orbit determination (OD) using a traditional Kalman filter. One approach to account for these errors is to consider them in the so-called Schmidt-Kalman filter (SKF), by augmenting the state covariance matrix (CM) with additional parameter covariance rather than additively estimating these so-called "consider" parameters. This paper introduces a new SKF algorithm with polynomial chaos expansion (PCE-SKF). The PCE approach has been proved to be more efficient than Monte Carlo method for propagating the input uncertainties onto the system response without experiencing any constraints of linear dynamics, or Gaussian distributions of the uncertainty sources. The state and covariance needed in the orbit prediction step are propagated using PCE. An inclined geosynchronous orbit scenario is set up to test the proposed PCE-SKF based OD algorithm. The satellite orbit is propagated based on numerical integration, with the uncertain coefficient of solar radiation pressure considered. The PCE-SKF solutions are compared with extended Kalman filter (EKF), SKF and PCE-EKF (EKF with PCE) solutions. It is implied that the covariance propagation using PCE leads to more precise OD solutions in comparison with those based on linear propagation of covariance.
A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.
Kamrunnahar, M; Schiff, S J
2011-01-01
We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.
Monitoring hydraulic fractures: state estimation using an extended Kalman filter
International Nuclear Information System (INIS)
Rochinha, Fernando Alves; Peirce, Anthony
2010-01-01
There is considerable interest in using remote elastostatic deformations to identify the evolving geometry of underground fractures that are forced to propagate by the injection of high pressure viscous fluids. These so-called hydraulic fractures are used to increase the permeability in oil and gas reservoirs as well as to pre-fracture ore-bodies for enhanced mineral extraction. The undesirable intrusion of these hydraulic fractures into environmentally sensitive areas or into regions in mines which might pose safety hazards has stimulated the search for techniques to enable the evolving hydraulic fracture geometries to be monitored. Previous approaches to this problem have involved the inversion of the elastostatic data at isolated time steps in the time series provided by tiltmeter measurements of the displacement gradient field at selected points in the elastic medium. At each time step, parameters in simple static models of the fracture (e.g. a single displacement discontinuity) are identified. The approach adopted in this paper is not to regard the sequence of sampled elastostatic data as independent, but rather to treat the data as linked by the coupled elastic-lubrication equations that govern the propagation of the evolving hydraulic fracture. We combine the Extended Kalman Filter (EKF) with features of a recently developed implicit numerical scheme to solve the coupled free boundary problem in order to form a novel algorithm to identify the evolving fracture geometry. Numerical experiments demonstrate that, despite excluding significant physical processes in the forward numerical model, the EKF-numerical algorithm is able to compensate for the un-modeled dynamics by using the information fed back from tiltmeter data. Indeed the proposed algorithm is able to provide reasonably faithful estimates of the fracture geometry, which are shown to converge to the actual hydraulic fracture geometry as the number of tiltmeters is increased. Since the location of
Energy Technology Data Exchange (ETDEWEB)
Pike, D.H.; Morrison, G.W.; Westley, G.W.
1977-10-01
The feasibility of using modern state estimation techniques (specifically Kalman Filtering and Linear Smoothing) to detect losses of material from material balance areas is evaluated. It is shown that state estimation techniques are not only feasible but in most situations are superior to existing methods of analysis. The various techniques compared include Kalman Filtering, linear smoothing, standard control charts, and average cumulative summation (CUSUM) charts. Analysis results indicated that the standard control chart is the least effective method for detecting regularly occurring losses. An improvement in the detection capability over the standard control chart can be realized by use of the CUSUM chart. Even more sensitivity in the ability to detect losses can be realized by use of the Kalman Filter and the linear smoother. It was found that the error-covariance matrix can be used to establish limits of error for state estimates. It is shown that state estimation techniques represent a feasible and desirable method of theft detection. The technique is usually more sensitive than the CUSUM chart in detecting losses. One kind of loss which is difficult to detect using state estimation techniques is a single isolated loss. State estimation procedures are predicated on dynamic models and are well-suited for detecting losses which occur regularly over several accounting periods. A single isolated loss does not conform to this basic assumption and is more difficult to detect.
A generalized polynomial chaos based ensemble Kalman filter with high accuracy
International Nuclear Information System (INIS)
Li Jia; Xiu Dongbin
2009-01-01
As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.
Mean-field Ensemble Kalman Filter
Law, Kody
2015-01-07
A proof of convergence of the standard EnKF generalized to non-Gaussian state space models is provided. A density-based deterministic approximation of the mean-field limiting EnKF (MFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for d < 2 . The fidelity of approximation of the true distribution is also established using an extension of total variation metric to random measures. This is limited by a Gaussian bias term arising from non-linearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
Data assimilation the ensemble Kalman filter
Evensen, Geir
2007-01-01
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should b...
Hesar, Hamed Danandeh; Mohebbi, Maryam
2017-05-01
In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed
Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability
Kar, Soummya; Moura, José M. F.
2011-04-01
The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.
Ensemble Kalman filtering with one-step-ahead smoothing
Raboudi, Naila F.
2018-01-11
The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.
Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering
Institute of Scientific and Technical Information of China (English)
FENG Yu-hu
2005-01-01
By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.
An improved fuzzy Kalman filter for state estimation of nonlinear systems
International Nuclear Information System (INIS)
Zhou, Z-J; Hu, C-H; Chen, L; Zhang, B-C
2008-01-01
The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method
Applying Kalman filtering to investigate tropospheric effects in VLBI
Soja, Benedikt; Nilsson, Tobias; Karbon, Maria; Heinkelmann, Robert; Liu, Li; Lu, Cuixian; Andres Mora-Diaz, Julian; Raposo-Pulido, Virginia; Xu, Minghui; Schuh, Harald
2014-05-01
Very Long Baseline Interferometry (VLBI) currently provides results, e.g., estimates of the tropospheric delays, with a delay of more than two weeks. In the future, with the coming VLBI2010 Global Observing System (VGOS) and increased usage of electronic data transfer, it is planned that the time between observations and results is decreased. This may, for instance, allow the integration of VLBI-derived tropospheric delays into numerical weather prediction models. Therefore, future VLBI analysis software packages need to be able to process the observational data autonomously in near real-time. For this purpose, we have extended the Vienna VLBI Software (VieVS) by a Kalman filter module. This presentation describes the filter and discusses its application for tropospheric studies. Instead of estimating zenith wet delays as piece-wise linear functions in a least-squares adjustment, the Kalman filter allows for more sophisticated stochastic modeling. We start with a random walk process to model the time-dependent behavior of the zenith wet delays. Other possible approaches include the stochastic model described by turbulence theory, e.g. the model by Treuhaft and Lanyi (1987). Different variance-covariance matrices of the prediction error, depending on the time of the year and the geographic latitude, have been tested. In winter and closer to the poles, lower variances and covariances are appropriate. The horizontal variations in tropospheric delays have been investigated by comparing three different strategies: assumption of a horizontally stratified troposphere, using north and south gradients modeled, e.g., as Gauss-Markov processes, and applying a turbulence model assuming correlations between observations in different azimuths. By conducting Monte-Carlo simulations of current standard VLBI networks and of future VGOS networks, the different tropospheric modeling strategies are investigated. For this purpose, we use the simulator module of VieVS which takes into
CSIR Research Space (South Africa)
Salmon, BP
2012-07-01
Full Text Available stream_source_info Salmon2_2012.pdf.txt stream_content_type text/plain stream_size 16400 Content-Encoding ISO-8859-1 stream_name Salmon2_2012.pdf.txt Content-Type text/plain; charset=ISO-8859-1 A SEARCH ALGORITHM TO META... the spectral bands separately and introduced a meta-optimization method for the EKF that will be called the Bias Variance Equilibrium Point (BVEP) in this paper. The objective of this paper is to introduce an unsuper- vised search algorithm called the Bias...
Vectorization of linear discrete filtering algorithms
Schiess, J. R.
1977-01-01
Linear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers. The square root filters are known to maintain a positive definite covariance matrix in cases in which the Kalman filter diverges due to ill-conditioning of the matrix. Vectorization of the filters is discussed, and comparisons are made of the number of operations and storage locations required by each filter. The Carlson filter is shown to be the most efficient of the filters on the Control Data STAR-100 computer.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-01-01
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361
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.
Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.
Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza
2018-03-01
This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering
International Nuclear Information System (INIS)
Poupon, C.; Roche, A.; Dubois, J.; Mangin, J.F.; Poupon, F.
2008-01-01
Diffusion magnetic resonance imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real-time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet. First, we introduce the general linear models corresponding to the two diffusion tensor and analytical Q-ball models of interest. Then, we present the Kalman filtering framework and we focus on the optimization of the diffusion orientation sets in order to speed up the convergence of the online processing. Last, we give some results on a healthy volunteer for the online tensor and the Q-ball model, and we make some comparisons with the conventional offline techniques used in the literature. We could achieve full real-time for diffusion tensor imaging and deferred time for Q-ball imaging, using a single workstation. (authors)
Kalman-fuzzy algorithm in short term load forecasting
International Nuclear Information System (INIS)
Shah Baki, S.R.; Saibon, H.; Lo, K.L.
1996-01-01
A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique
Extracting Steady State Components from Synchrophasor Data Using Kalman Filters
Directory of Open Access Journals (Sweden)
Farhan Mahmood
2016-04-01
Full Text Available Data from phasor measurement units (PMUs may be exploited to provide steady state information to the applications which require it. As PMU measurements may contain errors and missing data, the paper presents the application of a Kalman Filter technique for real-time data processing. PMU data captures the power system’s response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter methods have been applied to extract the steady state components of PMU measurements that can be fed to steady state applications. Two KF-based methods have been proposed, i.e., a windowing-based KF method and “the modified KF”. Both methods are capable of reducing noise, compensating for missing data and filtering outliers from input PMU signals. A comparison of proposed methods has been carried out using the PMU data generated from a hardware-in-the-loop (HIL experimental setup. In addition, a performance analysis of the proposed methods is performed using an evaluation metric.
Application of Unscented Kalman Filter in Satellite Orbit Simulation
Institute of Scientific and Technical Information of China (English)
ZHAO Dongming; CAI Zhiwu
2006-01-01
A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to non-linear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.
Vision-Based Position Estimation Utilizing an Extended Kalman Filter
2016-12-01
establishing the calibration mapping. A version of Kalman Filter was developed to minimize the impact of inaccuracies in the angle measurement as well...project error covariance ahead Z = [Ang_In; Alt_In]; % Measurements % Update Jacobian h11 = -XY_est(3,1)/(XY_est(1,1)^2+XY_est(3,1)^2); h13 ...XY_est(1,1)/(XY_est(1,1)^2+XY_est(3,1)^2); H = [h11 0 h13 0; 0 0 1 0]; Z_proj(1) = atan2(XY_proj(3),XY_proj(1)); % theta - predicted Z_proj(2
Adaptive training of feedforward neural networks by Kalman filtering
International Nuclear Information System (INIS)
Ciftcioglu, Oe.
1995-02-01
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Distributed Dynamic State Estimation with Extended Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry
2011-08-04
Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.
Application of extended Kalman filter to identification of enzymatic deactivation.
Caminal, G; Lafuente, J; López-Santín, J; Poch, M; Solà, C
1987-02-01
A recursive estimation scheme, the Extended Kalman Filter (EKF) technique, was applied to study enzymatic deactivation in the enzymatic hydrolysis of pretreated cellulose using a model previously developed by the authors. When no deactivation model was assumed, the results showed no variation with time for all the model parameters except for the maximum rate of cellobiose-to-glucose conversion (r'(m)).The r'(m) variation occurred in two zones with a grace period. A new model of enzymatic hydrolysis of pretreated cellulose deactivation was proposed and validated showing better behavior than the old deactivation model. This approach allows one to study enzyme deactivation without additional experiments and within operational conditions.
Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario
Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell
2010-01-01
Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.
Detection and estimation of sensor drifts using Kalman filters with a demonstration on a pressurizer
International Nuclear Information System (INIS)
Cho, Sungwhan; Jiang, Jin
2012-01-01
Highlights: ► How the expectation of the innovations changes in the drift case is formulated. ► Using the divergence in the expectation for detection of the drift is demonstrated. ► An augmented system model is proposed for estimation of the drift. ► Demonstration of the proposed algorithm is presented using a pressurizer model. - Abstract: An algorithm for detection and estimation of sensor drifts is proposed in this paper. The algorithm is based on estimation of the process states from which the measurements are made and the rate of drifts using a state augmented Kalman filter. The detection and the estimation of a drift are carried out by evaluating the mean of the innovation sequence of the Kalman filter. The relationship between the mean and the drift is analyzed in detail to provide insights on the connection between the innovation sequence and the drift. The developed algorithm has been successfully applied to a pressurizer for detection and estimation of pressure sensor drifts. The results convincingly demonstrate the capability of the algorithm.
A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking
Institute of Scientific and Technical Information of China (English)
ZHANG ZuTao; ZHANG JiaShu
2009-01-01
Non-Intrusive methods for eye tracking are Important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust-ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today's interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty In modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and im-prove the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.
Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter.
Palatella, Luigi; Trevisan, Anna
2015-04-01
When applied to strongly nonlinear chaotic dynamics the extended Kalman filter (EKF) is prone to divergence due to the difficulty of correctly forecasting the forecast error probability density function. In operational forecasting applications ensemble Kalman filters circumvent this problem with empirical procedures such as covariance inflation. This paper presents an extension of the EKF that includes nonlinear terms in the evolution of the forecast error estimate. This is achieved starting from a particular square-root implementation of the EKF with assimilation confined in the unstable subspace (EKF-AUS), that is, the span of the Lyapunov vectors with non-negative exponents. When the error evolution is nonlinear, the space where it is confined is no more restricted to the unstable and neutral subspace causing filter divergence. The algorithm presented here, denominated EKF-AUS-NL, includes the nonlinear terms in the error dynamics: These result from the nonlinear interaction among the leading Lyapunov vectors and account for all directions where the error growth may take place. Numerical results show that with the nonlinear terms included, filter divergence can be avoided. We test the algorithm on the Lorenz96 model, showing very promising results.
An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model.
Fan, Qigao; Zhang, Hai; Sun, Yan; Zhu, Yixin; Zhuang, Xiangpeng; Jia, Jie; Zhang, Pengsong
2018-05-02
Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study
Directory of Open Access Journals (Sweden)
Siavash Hosseinyalamdary
2018-04-01
Full Text Available Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU, have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.
Hosseinyalamdary, Siavash
2018-04-24
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time
Kelly, D. T B
2014-09-22
The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz \\'63 and \\'96 models, together with the incompressible Navier-Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier-Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.
A large-scale application of the Kalman alignment algorithm to the CMS tracker
International Nuclear Information System (INIS)
Widl, E; Fruehwirth, R
2008-01-01
The Kalman alignment algorithm has been specifically developed to cope with the demands that arise from the specifications of the CMS Tracker. The algorithmic concept is based on the Kalman filter formalism and is designed to avoid the inversion of large matrices. Most notably, the algorithm strikes a balance between conventional global and local track-based alignment algorithms, by restricting the computation of alignment parameters not only to alignable objects hit by the same track, but also to all other alignable objects that are significantly correlated. Nevertheless, this feature also comes with various trade-offs: Mechanisms are needed that affect which alignable objects are significantly correlated and keep track of these correlations. Due to the large amount of alignable objects involved at each update (at least compared to local alignment algorithms), the time spent for retrieving and writing alignment parameters as well as the required user memory becomes a significant factor. The large-scale test presented here applies the Kalman alignment algorithm to the (misaligned) CMS Tracker barrel, and demonstrates the feasibility of the algorithm in a realistic scenario. It is shown that both the computation time and the amount of required user memory are within reasonable bounds, given the available computing resources, and that the obtained results are satisfactory
A Network of Kalman Filters for MAI and ISI Compensation in a Non-Gaussian Environment
Directory of Open Access Journals (Sweden)
Sayadi Bessem
2005-01-01
Full Text Available This paper develops a new multiuser detector based on a network of kalman filters (NKF dealing with multiple access-interference (MAI, intersymbol Interference (ISI, and an impulsive observation noise. The two proposed schemes are based on the modeling of the DS-CDMA system by a discrete-time linear system that has non-Gaussian state and measurement noises. By approximating the non-Gaussian densities of the noises by a weighted sum of Gaussian terms and under the common MMSE estimation criterion, we first derive an NKF detector. This version is further optimized by introducing a feedback exploiting the ISI interference structure. The resulting scheme is an NKF detector based on a likelihood ratio test (LRT. Monte-Carlo simulations have shown that the NKF and the NKF based on LRT detectors significantly improve the efficiency and the performance of the classical Kalman algorithm.
Skew redundant MEMS IMU calibration using a Kalman filter
International Nuclear Information System (INIS)
Jafari, M; Sahebjameyan, M; Moshiri, B; Najafabadi, T A
2015-01-01
In this paper, a novel calibration procedure for skew redundant inertial measurement units (SRIMUs) based on micro-electro mechanical systems (MEMS) is proposed. A general model of the SRIMU measurements is derived which contains the effects of bias, scale factor error and misalignments. For more accuracy, the effect of lever arms of the accelerometers to the center of the table are modeled and compensated in the calibration procedure. Two separate Kalman filters (KFs) are proposed to perform the estimation of error parameters for gyroscopes and accelerometers. The predictive error minimization (PEM) stochastic modeling method is used to simultaneously model the effect of bias instability and random walk noise on the calibration Kalman filters to diminish the biased estimations. The proposed procedure is simulated numerically and has expected experimental results. The calibration maneuvers are applied using a two-axis angle turntable in a way that the persistency of excitation (PE) condition for parameter estimation is met. For this purpose, a trapezoidal calibration profile is utilized to excite different deterministic error parameters of the accelerometers and a pulse profile is used for the gyroscopes. Furthermore, to evaluate the performance of the proposed KF calibration method, a conventional least squares (LS) calibration procedure is derived for the SRIMUs and the simulation and experimental results compare the functionality of the two proposed methods with each other. (paper)
Direct and accelerated parameter mapping using the unscented Kalman filter.
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.
Method for Improving Indoor Positioning Accuracy Using Extended Kalman Filter
Directory of Open Access Journals (Sweden)
Seoung-Hyeon Lee
2016-01-01
Full Text Available Beacons using bluetooth low-energy (BLE technology have emerged as a new paradigm of indoor positioning service (IPS because of their advantages such as low power consumption, miniaturization, wide signal range, and low cost. However, the beacon performance is poor in terms of the indoor positioning accuracy because of noise, motion, and fading, all of which are characteristics of a bluetooth signal and depend on the installation location. Therefore, it is necessary to improve the accuracy of beacon-based indoor positioning technology by fusing it with existing indoor positioning technology, which uses Wi-Fi, ZigBee, and so forth. This study proposes a beacon-based indoor positioning method using an extended Kalman filter that recursively processes input data including noise. After defining the movement of a smartphone on a flat two-dimensional surface, it was assumed that the beacon signal is nonlinear. Then, the standard deviation and properties of the beacon signal were analyzed. According to the analysis results, an extended Kalman filter was designed and the accuracy of the smartphone’s indoor position was analyzed through simulations and tests. The proposed technique achieved good indoor positioning accuracy, with errors of 0.26 m and 0.28 m from the average x- and y-coordinates, respectively, based solely on the beacon signal.
Stock price estimation using ensemble Kalman Filter square root method
Karya, D. F.; Katias, P.; Herlambang, T.
2018-04-01
Shares are securities as the possession or equity evidence of an individual or corporation over an enterprise, especially public companies whose activity is stock trading. Investment in stocks trading is most likely to be the option of investors as stocks trading offers attractive profits. In determining a choice of safe investment in the stocks, the investors require a way of assessing the stock prices to buy so as to help optimize their profits. An effective method of analysis which will reduce the risk the investors may bear is by predicting or estimating the stock price. Estimation is carried out as a problem sometimes can be solved by using previous information or data related or relevant to the problem. The contribution of this paper is that the estimates of stock prices in high, low, and close categorycan be utilized as investors’ consideration for decision making in investment. In this paper, stock price estimation was made by using the Ensemble Kalman Filter Square Root method (EnKF-SR) and Ensemble Kalman Filter method (EnKF). The simulation results showed that the resulted estimation by applying EnKF method was more accurate than that by the EnKF-SR, with an estimation error of about 0.2 % by EnKF and an estimation error of 2.6 % by EnKF-SR.
ERP Estimation using a Kalman Filter in VLBI
Karbon, M.; Soja, B.; Nilsson, T.; Heinkelmann, R.; Liu, L.; Lu, C.; Mora-Diaz, J. A.; Raposo-Pulido, V.; Xu, M.; Schuh, H.
2014-12-01
Geodetic Very Long Baseline Interferometry (VLBI) is one of the primary space geodetic techniques, providing the full set of Earth Orientation Parameters (EOP), and it is unique for observing long term Universal Time (UT1). For applications such as satellite-based navigation and positioning, accurate and continuous ERP obtained in near real-time are essential. They also allow the precise tracking of interplanetary spacecraft. One of the goals of VGOS (VLBI Global Observing System) is to provide such near real-time ERP. With the launch of this next generation VLBI system, the International VLBI Service for Geodesy and Astrometry (IVS) increased its efforts not only to reach 1 mm accuracy on a global scale but also to reduce the time span between the collection of VLBI observations and the availability of the final results substantially. Project VLBI-ART contributes to these objectives by implementing an elaborate Kalman filter, which represents a perfect tool for analyzing VLBI data in quasi real-time. The goal is to implement it in the GFZ version of the Vienna VLBI Software (VieVS) as a completely automated tool, i.e., with no need for human interaction. Here we present the methodology and first results of Kalman filtered EOP from VLBI data.
ECG fiducial point extraction using switching Kalman filter.
Akhbari, Mahsa; Ghahjaverestan, Nasim Montazeri; Shamsollahi, Mohammad B; Jutten, Christian
2018-04-01
In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others. Copyright © 2018 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Xu Long; Wang Junping; Chen Quanshi
2012-01-01
Highlights: ► A novel extended Kalman Filtering SOC estimation method based on a stochastic fuzzy neural network (SFNN) battery model is proposed. ► The SFNN which has filtering effect on noisy input can model the battery nonlinear dynamic with high accuracy. ► A robust parameter learning algorithm for SFNN is studied so that the parameters can converge to its true value with noisy data. ► The maximum SOC estimation error based on the proposed method is 0.6%. - Abstract: Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second....... The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported...
Directory of Open Access Journals (Sweden)
O. Jakubov
2013-09-01
Full Text Available Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.
International Nuclear Information System (INIS)
Lee, Jeong Seong; Chung, Soon Il; Ljaz, Umer Zeeshan; Khambampati, Anil Kumar; Kim, Kyung Youn; Kim, Sin Kim
2007-01-01
For the visualization of the phase boundary in annular two-phase flows, the electrical resistance tomography (ERT) technique is introduced. In ERT, a set of predetermined electrical currents is injected trough the electrodes placed on the boundary of the flow passage and the induced electrical potentials are measured on the electrode. With the relationship between the injected currents and the induced voltages, the electrical conductivity distribution across the flow domain is estimated through the image reconstruction algorithm. In this, the conductivity distribution corresponds to the phase distribution. In the application of ERT to two-phase flows where there are only two conductivity values, the conductivity distribution estimation problem can be transformed into the boundary estimation problem. This paper considers a bubble boundary estimation with ERT in annular two-phase flows. As the image reconstruction algorithm, the unscented Kalman filter (UKF) is adopted since from the control theory it is reported that the UKF shows better performance than the extended Kalman filter (EKF) that has been commonly used. We formulated the UKF algorithm to be incorporate into the image reconstruction algorithm for the present problem. Also, phantom experiments have been conducted to evaluate the improvement by UKF
Kalman filter for statistical monitoring of forest cover across sub-continental regions
Raymond L. Czaplewski
1991-01-01
The Kalman filter is a multivariate generalization of the composite estimator which recursively combines a current direct estimate with a past estimate that is updated for expected change over time with a prediction model. The Kalman filter can estimate proportions of different cover types for sub-continental regions each year. A random sample of high-resolution...
Directory of Open Access Journals (Sweden)
Gerasimos G. Rigatos
2011-12-01
Full Text Available The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor
Autonomous underwater vehicle motion tracking using a Kalman filter for sensor fusion
CSIR Research Space (South Africa)
Holtzhausen, S
2008-11-01
Full Text Available it will be shown how a Kalman Filter is used to estimate the position of an autonomous vehicle in a three dimensional space. The Kalman filter is used to estimate movement and position using measurements from multiple sensors...
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Representing earthquake ground motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented Kalman filter and Kalman filter methods shows that unscented Kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than Kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power ofARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.
Diaz, Orlando X.
2010-01-01
Approved for public release; distribution is unlimited Two methods of estimating the attitude position of a spacecraft are examined in this thesis: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In particular, the UnScented QUaternion Estimator (USQUE) derived from [4] is implemented into a spacecraft model. For generalizations about the each of the filters, a simple problem is initially solved. These solutions display typical characteristics of each filter type. T...
Removal of jitter noise in 3D shape recovery from image focus by using Kalman filter.
Jang, Hoon-Seok; Muhammad, Mannan Saeed; Choi, Tae-Sun
2018-02-01
In regard to Shape from Focus, one critical factor impacting system application is mechanical vibration of the translational stage causing jitter noise along the optical axis. This noise is not detectable by simply observing the image. However, when focus measures are applied, inaccuracies in the depth occur. In this article, jitter noise and focus curves are modeled by Gaussian distribution and quadratic function, respectively. Then Kalman filter is designed and applied to eliminate this noise in the focus curves, as a post-processing step after the focus measure application. Experiments are implemented with simulated objects and real objects to show usefulness of proposed algorithm. © 2017 Wiley Periodicals, Inc.
Tao, Dongwang; Li, Hui; Ma, Qiang
2016-04-01
Complete structure identification of complicate nonlinear system using extend Kalman filter (EKF) or unscented Kalman filter (UKF) may have the problems of divergence, huge computation and low estimation precision due to the large dimension of the extended state space for the system. In this article, a decentralized identification method of hysteretic system based on the joint EKF and UKF is proposed. The complete structure is divided into linear substructures and nonlinear substructures. The substructures are identified from the top to the bottom. For the linear substructure, EKF is used to identify the extended space including the displacements, velocities, stiffness and damping coefficients of the substructures, using the limited absolute accelerations and the identified interface force above the substructure. Similarly, for the nonlinear substructure, UKF is used to identify the extended space including the displacements, velocities, stiffness, damping coefficients and control parameters for the hysteretic Bouc-Wen model and the force at the interface of substructures. Finally a 10-story shear-type structure with multiple inter-story hysteresis is used for numerical simulation and is identified using the decentralized approach, and the identified results are compared with those using only EKF or UKF for the complete structure identification. The results show that the decentralized approach has the advantage of more stability, relative less computation and higher estimation precision.
GPS Interference Mitigation Using Derivative-free Kalman Filter-based RNN
Directory of Open Access Journals (Sweden)
W. L. Mao
2016-09-01
Full Text Available The global positioning system (GPS with accurate positioning and timing properties has become integral part of all applications around the world. Radio frequency interference can significantly decrease the performance of GPS receivers or even completely prohibit the acquisition or tracking of satellites. The approaches of system performances that can be further enhanced by preprocessing to reject the jamming signal will be investigated. A recurrent neural network (RNN predictor for the GPS anti-jamming applications will be proposed. The adaptive RNN predictor is utilized to accurately predict the narrowband waveform based on an unscented Kalman filter (UKF-based algorithm. The UKF algorithm as a derivative-free alternative to the extended Kalman filter (EKF in the framework of state-estimation is adopted to achieve better performance in terms of convergence rate and quality of solution. The adaptive RNN filter can be successfully applied for the suppression of interference with a number of different narrowband formats, i.e. continuous wave interference (CWI, multi-tone CWI, swept CWI and pulsed CWI, to emulate realistic circumstances. Simulation results show that the proposed UKF-based scheme can offer the superior performances to suppress the interference over the conventional methods by computing mean squared prediction error (MSPE and signal-to-noise ratio (SNR improvements.
LHCb: Alignment of the LHCb Detector with Kalman Filter Fitted Tracks
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 ...
Tracking single dynamic MEG dipole sources using the projected Extended Kalman Filter.
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.
Extended Kalman filtering for joint mitigation of phase and amplitude noise in coherent QAM systems.
Pakala, Lalitha; Schmauss, Bernhard
2016-03-21
We numerically investigate our proposed carrier phase and amplitude noise estimation (CPANE) algorithm using extend Kalman filter (EKF) for joint mitigation of linear and non-linear phase noise as well as amplitude noise on 4, 16 and 64 polarization multiplexed (PM) quadrature amplitude modulation (QAM) 224 Gb/s systems. The results are compared to decision directed (DD) carrier phase estimation (CPE), DD phase locked loop (PLL) and universal CPE (U-CPE) algorithms. Besides eliminating the necessity of phase unwrapping function, EKF-CPANE shows improved performance for both back-to-back (BTB) and transmission scenarios compared to the aforementioned algorithms. We further propose a weighted innovation approach (WIA) of the EKF-CPANE which gives an improvement of 0.3 dB in the Q-factor, compared to the original algorithm.
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*
Hoteit, Ibrahim; Luo, Xiaodong; Pham, Dinh-Tuan
2012-01-01
introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa
2015-08-13
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
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)
Kalman and particle filtering methods for full vehicle and tyre identification
Bogdanski, Karol; Best, Matthew C.
2018-05-01
This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.
Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking
International Nuclear Information System (INIS)
Zu-Tao, Zhang; Jia-Shu, Zhang
2010-01-01
The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n + 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions. (classical areas of phenomenology)
Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels
Directory of Open Access Journals (Sweden)
Du Yong Kim
2012-01-01
Full Text Available We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.
Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter
Zhang, Qun; Yang, Yanfu; Zhong, Kangping; Liu, Jie; Wu, Xiong; Yao, Yong
2018-01-01
We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter (RD-LKF) by introducing the butterfly finite-impulse-response (FIR) filter in our previously proposed RD-LKF method. Along with the fast polarization tracking, it can also simultaneously compensate the inter-symbol interference (ISI) effects including residual chromatic dispersion and polarization mode dispersion. Compared with the conventional radius-directed equalizer (RDE) algorithm, it is demonstrated experimentally that three times faster convergence speed, one order of magnitude better tracking capability, and better BER performance is obtained in polarization division multiplexing 16 quadrature amplitude modulation system. Besides, the influences of the algorithm parameters on the convergence and the tracking performance are investigated by numerical simulation.
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
Application of Consider Covariance to the Extended Kalman Filter
Lundberg, John B.
1996-01-01
The extended Kalman filter (EKF) is the basis for many applications of filtering theory to real-time problems where estimates of the state of a dynamical system are to be computed based upon some set of observations. The form of the EKF may vary somewhat from one application to another, but the fundamental principles are typically unchanged among these various applications. As is the case in many filtering applications, models of the dynamical system (differential equations describing the state variables) and models of the relationship between the observations and the state variables are created. These models typically employ a set of constants whose values are established my means of theory or experimental procedure. Since the estimates of the state are formed assuming that the models are perfect, any modeling errors will affect the accuracy of the computed estimates. Note that the modeling errors may be errors of commission (errors in terms included in the model) or omission (errors in terms excluded from the model). Consequently, it becomes imperative when evaluating the performance of real-time filters to evaluate the effect of modeling errors on the estimates of the state.
On a nonlinear Kalman filter with simplified divided difference approximation
Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.
2012-01-01
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling's interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling's interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling's interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
Using Kalman Filters to Reduce Noise from RFID Location System
Xavier, José; Reis, Luís Paulo; Petry, Marcelo
2014-01-01
Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes—linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11–13% of improvement). PMID:24592186
On a nonlinear Kalman filter with simplified divided difference approximation
Luo, Xiaodong
2012-03-01
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling\\'s interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling\\'s interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling\\'s interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
Estimating ice-affected streamflow by extended Kalman filtering
Holtschlag, D.J.; Grewal, M.S.
1998-01-01
An extended Kalman filter was developed to automate the real-time estimation of ice-affected streamflow on the basis of routine measurements of stream stage and air temperature and on the relation between stage and streamflow during open-water (ice-free) conditions. The filter accommodates three dynamic modes of ice effects: sudden formation/ablation, stable ice conditions, and eventual elimination. The utility of the filter was evaluated by applying it to historical data from two long-term streamflow-gauging stations, St. John River at Dickey, Maine and Platte River at North Bend, Nebr. Results indicate that the filter was stable and that parameters converged for both stations, producing streamflow estimates that are highly correlated with published values. For the Maine station, logarithms of estimated streamflows are within 8% of the logarithms of published values 87.2% of the time during periods of ice effects and within 15% 96.6% of the time. Similarly, for the Nebraska station, logarithms of estimated streamflows are within 8% of the logarithms of published values 90.7% of the time and within 15% 97.7% of the time. In addition, the correlation between temporal updates and published streamflows on days of direct measurements at the Maine station was 0.777 and 0.998 for ice-affected and open-water periods, respectively; for the Nebraska station, corresponding correlations were 0.864 and 0.997.
Li, Linqian; Feng, Yiqiao; Zhang, Wenbo; Cui, Nan; Xu, Hengying; Tang, Xianfeng; Xi, Lixia; Zhang, Xiaoguang
2017-07-01
A joint carrier recovery scheme for polarization division multiplexing (PDM) coherent optical transmission system is proposed and demonstrated, in which the extended Kalman filter (EKF) is exploited to estimate and equalize the carrier frequency offset (CFO) and carrier phase noise (CPN) simultaneously. The proposed method is implemented and verified in the PDM-QPSK system and the PDM-16QAM system with the comparisons to conventional improved Mth-power (IMP) algorithm for CFO estimation, blind phase search (BPS) algorithm or Viterbi-Viterbi (V-V) algorithm for CPN recovery. It is demonstrated that the proposed scheme shows high CFO estimation accuracy, with absolute mean estimation error below 1.5 MHz. Meanwhile, the proposed method has the CFO tolerance of [±3 GHz] for PDM-QPSK system and [±0.9 GHz] for PDM-16QAM system. Compare with IMP/BPS and IMP/V-V, the proposed scheme can enhance the linewidth symbol duration product from 3 × 10-4 (IMP/BPS) and 2 × 10-4 (IMP/V-V) to 1 × 10-3 for PDM-QPSK, and from 1 × 10-4 (IMP/BPS) to 3 × 10-4 for PDM-16QAM, respectively, at the 1 dB optical signal-to-noise ratio (OSNR) penalty. The proposed Kalman filter also shows a fast convergence with only 100 symbols and much lower computational complexity.
Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters.
Song, Jin Woo; Park, Chan Gook
2018-04-21
An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU. In order to obtain the course measurements, the filter uses magnetic sensors and a position-trace based course angle. For preventing magnetic disturbance from contaminating the estimation, the magnetic sensors are attached to the waistband. Because the course angle error is mainly due to the heading error of the IMU, and the characteristic error of the heading angle is highly dependent on that of the course angle, the estimated course angle error is used as a measurement for estimating the heading error in the second stage filter. At the second stage, an inertial navigation system-extended Kalman filter-ZUPT (INS-EKF-ZUPT) method is adopted. As the heading error is estimated directly by using course-angle error measurements, the estimation accuracy for the heading and yaw gyro bias can be enhanced, compared with the ZUPT-only case, which eventually enhances the position accuracy more efficiently. The performance enhancements are verified via experiments, and the way-point position error for the proposed method is compared with those for the ZUPT-only case and with other cases that use ZUPT and various types of magnetic heading measurements. The results show that the position errors are reduced by a maximum of 90% compared with the conventional ZUPT based PDR algorithms.
DEFF Research Database (Denmark)
Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin
2017-01-01
Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic...... system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA...
Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system
Institute of Scientific and Technical Information of China (English)
HAO Yan-ling; CHEN Ming-hui; LI Liang-jun; XU Bo
2008-01-01
There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system.This paper discussed the use of GPS,but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS).One method is based on the Kalman filter (KF),and the other is based on the robust filter.Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF,given substantial process noise or unknown noise statistics.So the robust filter is an effective and useful method for initial alignment of SINS.This research should make the use of SINS more popular,and is also a step for further research.
Performance reliability prediction for thermal aging based on kalman filtering
International Nuclear Information System (INIS)
Ren Shuhong; Wen Zhenhua; Xue Fei; Zhao Wensheng
2015-01-01
The performance reliability of the nuclear power plant main pipeline that failed due to thermal aging was studied by the performance degradation theory. Firstly, through the data obtained from the accelerated thermal aging experiments, the degradation process of the impact strength and fracture toughness of austenitic stainless steel material of the main pipeline was analyzed. The time-varying performance degradation model based on the state space method was built, and the performance trends were predicted by using Kalman filtering. Then, the multi-parameter and real-time performance reliability prediction model for the main pipeline thermal aging was developed by considering the correlation between the impact properties and fracture toughness, and by using the stochastic process theory. Thus, the thermal aging performance reliability and reliability life of the main pipeline with multi-parameter were obtained, which provides the scientific basis for the optimization management of the aging maintenance decision making for nuclear power plant main pipelines. (authors)
Kalman Filter Track Fits and Track Breakpoint Analysis
Astier, Pierre; Cousins, R D; Letessier-Selvon, A A; Popov, B A; Vinogradova, T G; Astier, Pierre; Cardini, Alessandro; Cousins, Robert D.; Letessier-Selvon, Antoine; Popov, Boris A.; Vinogradova, Tatiana
2000-01-01
We give an overview of track fitting using the Kalman filter method in the NOMAD detector at CERN, and emphasize how the wealth of by-product information can be used to analyze track breakpoints (discontinuities in track parameters caused by scattering, decay, etc.). After reviewing how this information has been previously exploited by others, we describe extensions which add power to breakpoint detection and characterization. We show how complete fits to the entire track, with breakpoint parameters added, can be easily obtained from the information from unbroken fits. Tests inspired by the Fisher F-test can then be used to judge breakpoints. Signed quantities (such as change in momentum at the breakpoint) can supplement unsigned quantities such as the various chisquares. We illustrate the method with electrons from real data, and with Monte Carlo simulations of pion decays.
Quantum Kalman filtering and the Heisenberg limit in atomic magnetometry
Energy Technology Data Exchange (ETDEWEB)
Geremia, J M; Stockton, John K; Doherty, Andrew C; Mabuchi, Hideo [Norman Bridge Laboratory of Physics, California Institute of Technology, Pasadena, California, 91125 (United States)
2003-12-19
The shot-noise detection limit in current high-precision magnetometry [I. Kominis, T. Kornack, J. Allred, and M. Romalis, Nature (London) 422, 596 (2003)]10.1038/nature01484 is a manifestation of quantum fluctuations that scale as 1/{radical}(N) in an ensemble of N atoms. Here, we develop a procedure that combines continuous measurement and quantum Kalman filtering [V. Belavkin, Rep. Math. Phys. 43, 405 (1999)] to surpass this conventional limit by exploiting conditional spin squeezing to achieve 1/N field sensitivity. Our analysis demonstrates the importance of optimal estimation for high bandwidth precision magnetometry at the Heisenberg limit and also identifies an approximate estimator based on linear regression.
Kalman Filter for Mass Property and Thrust Identification (MMS)
Queen, Steven
2015-01-01
The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories, elliptically orbiting the Earth in a tetrahedron formation. For the operational success of the mission, on-board systems must be able to deliver high-precision orbital adjustment maneuvers. On MMS, this is accomplished using feedback from on-board star sensors in tandem with accelerometers whose measurements are dynamically corrected for errors associated with a spinning platform. In order to determine the required corrections to the measured acceleration, precise estimates of attitude, rate, and mass-properties is necessary. To this end, both an on-board and ground-based Multiplicative Extended Kalman Filter (MEKF) were formulated and implemented in order to estimate the dynamic and quasi-static properties of the spacecraft.
Model Calibration of Exciter and PSS Using Extended Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Kalsi, Karanjit; Du, Pengwei; Huang, Zhenyu
2012-07-26
Power system modeling and controls continue to become more complex with the advent of smart grid technologies and large-scale deployment of renewable energy resources. As demonstrated in recent studies, inaccurate system models could lead to large-scale blackouts, thereby motivating the need for model calibration. Current methods of model calibration rely on manual tuning based on engineering experience, are time consuming and could yield inaccurate parameter estimates. In this paper, the Extended Kalman Filter (EKF) is used as a tool to calibrate exciter and Power System Stabilizer (PSS) models of a particular type of machine in the Western Electricity Coordinating Council (WECC). The EKF-based parameter estimation is a recursive prediction-correction process which uses the mismatch between simulation and measurement to adjust the model parameters at every time step. Numerical simulations using actual field test data demonstrate the effectiveness of the proposed approach in calibrating the parameters.
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, Lynn
2013-08-08
Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have
Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter.
Song, Xuegang; Zhang, Yuexin; Liang, Dakai
2017-10-10
This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.
Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Xuegang Song
2017-10-01
Full Text Available This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.
Rotation speed measurement for turbine governor: torsion filtering by using Kalman filter
International Nuclear Information System (INIS)
Houry, M.P.; Bourles, H.
1995-11-01
The rotation speed of a turbogenerator is disturbed by its shaft torsion. Obtaining a filtered measure of this sped a problem of a great practical importance for turbine governor. A good filtering of this speed must meet two requirements: it must cut frequencies of the shaft torsion oscillation and it must not reduce or delay the signal in the pass-band. i.e. at lower frequencies. At Electricite de France, the speed measure is used to set in motion the fast valving system as quickly as possible, after a short circuit close to the unit (to contribute to the stability) or after an islanding (to quickly reach a balance with the house load). It is difficult to satisfy these two requirements by using conventional filtering methods. The standard solution consists in a first order filter: at Electricite de France, its time constant is equal to 80 ms; We have decided to improve this filtering by designing a new filter which cuts the frequencies of the shaft torsion oscillation without reducing the bandwidth of the speed measure. If one uses conventional methods to obtain a band-stop filter (for instance a Butterworth, a Chebyshev or an elliptic band-stop filter),it is easy to obtain the desired magnitude but not a phase near zero in the whole pass-band. Therefore, we have chosen to design the filter by using Kalman's theory. The measurement noise is modeled as a colored one, generated by a very lightly damped system driven by a white noise. The resulting Kalman filter is an effective band-stop filter, whose phase nicely remains near zero in the whole pass-band. (authors). 13 refs., 12 figs
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.
Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction.
Lam, Frank; Lu, Hsiang-Wei; Wu, Chung-Che; Aliyazicioglu, Zekeriya; Kang, James S
2017-01-01
Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C , peripheral resistance R , aortic impedance r , and the inertia of blood L , to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies.
Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.
Joukov, Vladimir; Bonnet, Vincent; Karg, Michelle; Venture, Gentiane; Kulic, Dana
2018-02-01
This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4° root mean squared error, and segments the motion into repetitions with 96% accuracy.
Delay Kalman Filter to Estimate the Attitude of a Mobile Object with Indoor Magnetic Field Gradients
Directory of Open Access Journals (Sweden)
Christophe Combettes
2016-05-01
Full Text Available More and more services are based on knowing the location of pedestrians equipped with connected objects (smartphones, smartwatches, etc.. One part of the location estimation process is attitude estimation. Many algorithms have been proposed but they principally target open space areas where the local magnetic field equals the Earth’s field. Unfortunately, this approach is impossible indoors, where the use of magnetometer arrays or magnetic field gradients has been proposed. However, current approaches omit the impact of past state estimates on the current orientation estimate, especially when a reference field is computed over a sliding window. A novel Delay Kalman filter is proposed in this paper to integrate this time correlation: the Delay MAGYQ. Experimental assessment, conducted in a motion lab with a handheld inertial and magnetic mobile unit, shows that the novel filter better estimates the Euler angles of the handheld device with an 11.7° mean error on the yaw angle as compared to 16.4° with a common Additive Extended Kalman filter.
Rotation speed measurement for turbine governor: torsion filtering by using Kalman filter
International Nuclear Information System (INIS)
Houry, M.P.; Bourles, H.
1996-01-01
The rotation speed of a turbogenerator is disturbed by its shaft torsion. Obtaining a filtered measure of this speed is a problem of a great practical importance for turbine governor. A good filtering of this speed must meet two requirements: it must cut frequencies of the shaft torsion oscillation and it must not reduce or delay the signal in the pass-band, i.e. at lower frequencies. At Electricite de France, the speed measure is used to set in motion the fast valving system as quickly as possible, after a short circuit close to the unit or rather an islanding. It is difficult to satisfy these two requirements by using conventional filtering methods. The standard solution consists in a first order filter: at Electricite de France, its time constant is equal to 80 ms. We have decided to improve this filtering by designing a new filter which cuts the frequencies of the shaft torsion oscillation without reducing the bandwidth to the speed measure. If one uses conventional methods to obtain a band stop filter, it is easy to obtain the desired magnitude but not a phase near zero in the whole pass-band. Therefore, we have chosen to design the filter by using Kalman'a theory. The measurement noise is modeled as a colored one, generated by a very lightly damped system driven by a while noise. The resulting Kalman filter is an effective band stop filter, whose phase nicely remains near zero in the whole pass-band. The digital simulations we made and the tests we carried out with the Electricite de France Micro Network laboratory show the advantages of the rotation speed filter we designed using Kalman's theory. With the proposed filter, the speed measure filtering is better in terms of reduction and phase shift. the result is that there are less untimely solicitations of the fast valving system. Consequently, this device improves the power systems stability by minimizing the risks of deep perturbations due to a temporary lack of generation and the risks of under-speed loss
Dreano, Denis
2015-04-27
A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
Dreano, Denis; Mallick, Bani; Hoteit, Ibrahim
2015-01-01
A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
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.
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.
Dual extended Kalman filter for combined estimation of vehicle state and road friction
Zong, Changfu; Hu, Dan; Zheng, Hongyu
2013-03-01
Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.
Fast algorithm for Morphological Filters
International Nuclear Information System (INIS)
Lou Shan; Jiang Xiangqian; Scott, Paul J
2011-01-01
In surface metrology, morphological filters, which evolved from the envelope filtering system (E-system) work well for functional prediction of surface finish in the analysis of surfaces in contact. The naive algorithms are time consuming, especially for areal data, and not generally adopted in real practice. A fast algorithm is proposed based on the alpha shape. The hull obtained by rolling the alpha ball is equivalent to the morphological opening/closing in theory. The algorithm depends on Delaunay triangulation with time complexity O(nlogn). In comparison to the naive algorithms it generates the opening and closing envelope without combining dilation and erosion. Edge distortion is corrected by reflective padding for open profiles/surfaces. Spikes in the sample data are detected and points interpolated to prevent singularities. The proposed algorithm works well both for morphological profile and area filters. Examples are presented to demonstrate the validity and superiority on efficiency of this algorithm over the naive algorithm.
Derivative free Kalman filtering used for orchard navigation
DEFF Research Database (Denmark)
Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian
2010-01-01
representing the surrounding fruit trees. The line features are created on basis of 2D laser scanner data by a least square algorithm. The Matlab (R) toolbox Kalmtool is used for easy switching between different filter implementations without the need for changing the base structure of the system....
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.
Solid-state lighting life prediction using extended Kalman filter
Energy Technology Data Exchange (ETDEWEB)
Lall, Pradeep [Auburn Univ., AL (United States); Wei, Junchao [Auburn Univ., AL (United States); Davis, Lynn [RTI International, Durham, NC (United States)
2013-07-16
Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understanding and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products to reduce overall energy consumption attributable to lighting. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of SSL Luminaires from LM-80 test data. The TM-21 model uses an Arrhenius Equation with an Activation Energy, Pre-decay factor and Decay Rates. Several failure mechanisms may be active in a luminaire at a single time causing lumen depreciation. The underlying TM-21 Arrhenius Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, a Kalman Filter and Extended Kalman Filters have been used to develop a 70% Lumen Maintenance Life Prediction Model for a LEDs used in SSL luminaires. This model can be used to calculate acceleration factors, evaluate failure-probability and identify ALT methodologies for reducing test time. Ten-thousand hour LM
Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time
International Nuclear Information System (INIS)
Kelly, D T B; Stuart, A M; Law, K J H
2014-01-01
The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz '63 and '96 models, together with the incompressible Navier–Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier–Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise. (paper)
Simultaneous pattern recognition and track fitting by the Kalman filtering method
International Nuclear Information System (INIS)
Billoir, P.
1990-01-01
A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. The fitted parameters and weight matrix of the candidate track are updated when adding a point, and give an increasing precision on prediction of the next point. Thus, pattern recognition and track fitting can be accomplished simultaneously. The method has been implemented and tested for track reconstruction for the vertex detector of the ZEUS experiment at DESY. Detailed procedures of the method and its performance are presented. Its flexibility is described as well. (orig.)
Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.
Roujol, Sébastien; de Senneville, Baudouin Denis; Hey, Silke; Moonen, Chrit; Ries, Mario
2012-03-01
Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.
A novel extended Kalman filter for a class of nonlinear systems
Institute of Scientific and Technical Information of China (English)
DONG Zhe; YOU Zheng
2006-01-01
Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF.
Borodachev, S. M.
2016-06-01
The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.
Extended Kalman filtering applied to a two-axis robotic arm with flexible links
Energy Technology Data Exchange (ETDEWEB)
Lertpiriyasuwat, V.; Berg, M.C.; Buffinton, K.W.
2000-03-01
An industrial robot today uses measurements of its joint positions and models of its kinematics and dynamics to estimate and control its end-effector position. Substantially better end-effector position estimation and control performance would be obtainable if direct measurements of its end-effector position were also used. The subject of this paper is extended Kalman filtering for precise estimation of the position of the end-effector of a robot using, in addition to the usual measurements of the joint positions, direct measurements of the end-effector position. The estimation performances of extended Kalman filters are compared in applications to a planar two-axis robotic arm with very flexible links. The comparisons shed new light on the dependence of extended Kalman filter estimation performance on the quality of the model of the arm dynamics that the extended Kalman filter operates with.
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick
2015-08-01
Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.
National Research Council Canada - National Science Library
Erwin, R. S; Bernstein, Dennis S
2005-01-01
.... In this paper we use a sampled-data extended Kalman Filter to estimate the trajectory or a target satellite when only range measurements are available from a constellation or orbiting spacecraft...
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.
Application of Kalman Filter for Estimating a Process Disturbance in a Building Space
Directory of Open Access Journals (Sweden)
Deuk-Woo Kim
2017-10-01
Full Text Available This paper addresses an application of the Kalman filter for estimating a time-varying process disturbance in a building space. The process disturbance means a synthetic composite of heat gains and losses caused by internal heat sources e.g., people, lights, equipment, and airflows. It is difficult to measure and quantify the internal heat sources and airflows due to their dynamic nature and time-lag impact on indoor environment. To address this issue, a Kalman filter estimation method was used in this study. The Kalman filtering is well suited for situations when state variables of interest cannot be measured. Based on virtual and real experiments conducted in this study, it was found that the Kalman filter can be used to estimate the time-varying process disturbance in a building space.
Ledsham, W. H.; Staelin, D. H.
1978-01-01
An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Kalman filtering techniques for reducing variance of digital speckle displacement measurement noise
Institute of Scientific and Technical Information of China (English)
Donghui Li; Li Guo
2006-01-01
@@ Target dynamics are assumed to be known in measuring digital speckle displacement. Use is made of a simple measurement equation, where measurement noise represents the effect of disturbances introduced in measurement process. From these assumptions, Kalman filter can be designed to reduce variance of measurement noise. An optical and analysis system was set up, by which object motion with constant displacement and constant velocity is experimented with to verify validity of Kalman filtering techniques for reduction of measurement noise variance.
Institute of Scientific and Technical Information of China (English)
Li Shu; Zhuo Jiashou; Ren Qingwen
2000-01-01
In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.
Directory of Open Access Journals (Sweden)
Gerasimos G. Rigatos
2011-12-01
Full Text Available The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. Sensorless control of the induction motor is again implemented through feedback of the estimated state vector. Additionally, a state estimation-based control loop is implemented using the Unscented Kalman Filter. Moreover, state estimation-based control is developed for the induction motor model using a nonlinear flatness-based controller and the state estimation that is provided by the Extended Kalman Filter. Unlike field oriented control, in the latter approach there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. The efficiency of the Kalman Filter-based control schemes, for both the DC and induction motor models, is evaluated through simulation experiments.
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
International Nuclear Information System (INIS)
Noda, S.; Sasashige, K.; Katagami, D.; Ushio, T.; Kubota, T.; Okamoto, K.; Iida, Y.; Kida, S.; Shige, S.; Shimomura, S.; Aonashi, K.; Inoue, T.; Morimoto, T.; Kawasaki, Z.
2007-01-01
Estimates of precipitation at a high time and space resolution are required for many important applications. In this paper, a new global precipitation map with high spatial (0.1 degree) and temporal (1 hour) resolution using Kalman filter technique is presented and evaluated. Infrared radiometer data, which are available globally nearly everywhere and nearly all the time from geostationary orbit, are used with the several microwave radiometers aboard the LEO satellites. IR data is used as a means to move the precipitation estimates from microwave observation during periods when microwave data are not available at a given location. Moving vector is produced by computing correlations on successive images of IR data. When precipitation is moved, the Kalman filter is applied for improving the moving technique in this research. The new approach showed a better score than the technique without Kalman filter. The correlation coefficient was 0.1 better than without the Kalman filter about 6 hours after the last microwave overpasses, and the RMS error was improved about 0.1 mm/h with the Kalman filter technique. This approach is unique in that 1) the precipitation estimates from the microwave radiometer is mainly used, 2) the IR temperature in every hour is also used for the precipitation estimates based on the Kalman filter theory
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.
Vehicle State Information Estimation with the Unscented Kalman Filter
Directory of Open Access Journals (Sweden)
Hongbin Ren
2014-01-01
Full Text Available The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF, since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future.
Discrete Kalman Filter based Sensor Fusion for Robust Accessibility Interfaces
International Nuclear Information System (INIS)
Ghersi, I; Miralles, M T; Mariño, M
2016-01-01
Human-machine interfaces have evolved, benefiting from the growing access to devices with superior, embedded signal-processing capabilities, as well as through new sensors that allow the estimation of movements and gestures, resulting in increasingly intuitive interfaces. In this context, sensor fusion for the estimation of the spatial orientation of body segments allows to achieve more robust solutions, overcoming specific disadvantages derived from the use of isolated sensors, such as the sensitivity of magnetic-field sensors to external influences, when used in uncontrolled environments. In this work, a method for the combination of image-processing data and angular-velocity registers from a 3D MEMS gyroscope, through a Discrete-time Kalman Filter, is proposed and deployed as an alternate user interface for mobile devices, in which an on-screen pointer is controlled with head movements. Results concerning general performance of the method are presented, as well as a comparative analysis, under a dedicated test application, with results from a previous version of this system, in which the relative-orientation information was acquired directly from MEMS sensors (3D magnetometer-accelerometer). These results show an improved response for this new version of the pointer, both in terms of precision and response time, while keeping many of the benefits that were highlighted for its predecessor, giving place to a complementary method for signal acquisition that can be used as an alternative-input device, as well as for accessibility solutions. (paper)
Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering
Panomruttanarug, Benjamas; Higuchi, Kohji
This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.
Dynamics of Electricity Demand in Lesotho: A Kalman Filter Approach
Directory of Open Access Journals (Sweden)
Thamae Retselisitsoe Isaiah
2015-04-01
Full Text Available This study provides an empirical analysis of the time-varying price and income elasticities of electricity demand in Lesotho for the period 1995-2012 using the Kalman filter approach. The results reveal that economic growth has been one of the main drivers of electricity consumption in Lesotho while electricity prices are found to play a less significant role since they are monopoly-driven and relatively low when compared to international standards. These findings imply that increases in electricity prices in Lesotho might not have a significant impact on consumption in the short-run. However, if the real electricity prices become too high over time, consumers might change their behavior and sensitivity to price and hence, energy policymakers will need to reconsider their impact in the long-run. Furthermore, several exogenous shocks seem to have affected the sensitivity of electricity demand during the period prior to regulation, which made individuals, businesses and agencies to be more sensitive to electricity costs. On the other hand, the period after regulation has been characterized by more stable and declining sensitivity of electricity demand. Therefore, factors such as regulation and changes in the country’s economic activities appear to have affected both price and income elasticities of electricity demand in Lesotho.
Rainfall estimation with TFR model using Ensemble Kalman filter
Asyiqotur Rohmah, Nabila; Apriliani, Erna
2018-03-01
Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.
IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter
Cho, K.; Hyoung-Wook, C.; Jo, Y.
2016-12-01
Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.
Streamflow data assimilation in SWAT model using Extended Kalman Filter
Sun, Leqiang; Nistor, Ioan; Seidou, Ousmane
2015-12-01
The Extended Kalman Filter (EKF) is coupled with the Soil and Water Assessment Tools (SWAT) model in the streamflow assimilation of the upstream Senegal River in West Africa. Given the large number of distributed variables in SWAT, only the average watershed scale variables are included in the state vector and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Both the soil moisture and CN2 are significantly updated in the wet season, yet they have opposite update patterns. A case study for a large flood forecast shows that for up to seven days, the streamflow forecast is moderately improved using the EKF-subsequent open loop scheme but significantly improved with a newly designed quasi-error update scheme. The former has better performances in the flood rising period while the latter has better performances in the recession period. For both schemes, the streamflow forecast is improved more significantly when the lead time is shorter.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
A Fixed-Lag Kalman Smoother to Filter Power Line Interference in Electrocardiogram Recordings.
Warmerdam, G J J; Vullings, R; Schmitt, L; Van Laar, J O E H; Bergmans, J W M
2017-08-01
Filtering power line interference (PLI) from electrocardiogram (ECG) recordings can lead to significant distortions of the ECG and mask clinically relevant features in ECG waveform morphology. The objective of this study is to filter PLI from ECG recordings with minimal distortion of the ECG waveform. In this paper, we propose a fixed-lag Kalman smoother with adaptive noise estimation. The performance of this Kalman smoother in filtering PLI is compared to that of a fixed-bandwidth notch filter and several adaptive PLI filters that have been proposed in the literature. To evaluate the performance, we corrupted clean neonatal ECG recordings with various simulated PLI. Furthermore, examples are shown of filtering real PLI from an adult and a fetal ECG recording. The fixed-lag Kalman smoother outperforms other PLI filters in terms of step response settling time (improvements that range from 0.1 to 1 s) and signal-to-noise ratio (improvements that range from 17 to 23 dB). Our fixed-lag Kalman smoother can be used for semi real-time applications with a limited delay of 0.4 s. The fixed-lag Kalman smoother presented in this study outperforms other methods for filtering PLI and leads to minimal distortion of the ECG waveform.
Directory of Open Access Journals (Sweden)
Feng Lian
2012-01-01
Full Text Available The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD filter and its extended Kalman (EK filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.
Kalman filter based fault diagnosis of networked control system with white noise
Institute of Scientific and Technical Information of China (English)
Yanwei WANG; Ying ZHENG
2005-01-01
The networked control system NCS is regarded as a sampled control system with output time-variant delay.White noise is considered in the model construction of NCS.By using the Kalman filter theory to compute the filter parameters,a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults.Finally,an example is given to show the feasibility of the approach.
Directory of Open Access Journals (Sweden)
Nataliya Chukhrova
2017-05-01
Full Text Available This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.
International Nuclear Information System (INIS)
Cooper, W.S.
1986-01-01
Several techniques proposed for diagnosing the velocity distribution of fast alpha-particles in a burning plasma require the injection of a beam of fast neutral atoms as probes. The author discusses how improving signal detection techniques is a high leverage factor in reducing the cost of the diagnostic beam. Optimal estimation theory provides a computational algorithm, the Kalman filter, that can optimally estimate the amplitude of a signal with arbitrary (but known) time dependence in the presence of noise. In one example presented, based on a square-wave signal and assumed noise levels, the Kalman filter achieves an enhancement of signal detection efficiency of about a factor of 10 (as compared with the straightforward observation of the signal superimposed on noise) with an observation time of 100 signal periods
On the evaluation of uncertainties for state estimation with the Kalman filter
International Nuclear Information System (INIS)
Eichstädt, S; Makarava, N; Elster, C
2016-01-01
The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used. This is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The ‘Guide to the Expression of Uncertainty in Measurement’ (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can then be reached. For precisely known nonlinear systems, though, the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of the GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a 2D dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings. (paper)
Constraining the ensemble Kalman filter for improved streamflow forecasting
Maxwell, Deborah H.; Jackson, Bethanna M.; McGregor, James
2018-05-01
Data assimilation techniques such as the Ensemble Kalman Filter (EnKF) are often applied to hydrological models with minimal state volume/capacity constraints enforced during ensemble generation. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this paper, we investigate the effect of constraining the EnKF on forecast performance. A "free run" in which no assimilation is applied is compared to a completely unconstrained EnKF implementation, a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then to a more tightly constrained implementation where flux as well as mass constraints are imposed to force the rate of water movement to/from ensemble states to be within physically consistent boundaries. A three year period (2008-2010) was selected from the available data record (1976-2010). This was specifically chosen as it had no significant data gaps and represented well the range of flows observed in the longer dataset. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Mass constraints alone did little to improve forecast performance; in fact, several were significantly degraded compared to the free run. In contrast, the combined use of mass and flux constraints significantly improved forecast performance in six events relative to all other implementations, while the remaining two events showed no significant difference in performance. Placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state estimation and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also
Directory of Open Access Journals (Sweden)
Yongliang Sun
2013-11-01
Full Text Available A Kalman/map filtering (KMF-aided fast normalized cross correlation (FNCC-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.
Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin
2013-11-13
A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.
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.
Luo, Xiaodong
2014-10-01
The ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In previous studies, the authors proposed an observation-space-based strategy, called residual nudging, to improve the stability of the EnKF when dealing with linear observation operators. The main idea behind residual nudging is to monitor and, if necessary, adjust the distances (misfits) between the real observations and the simulated ones of the state estimates, in the hope that by doing so one may be able to obtain better estimation accuracy. In the present study, residual nudging is extended and modified in order to handle nonlinear observation operators. Such extension and modification result in an iterative filtering framework that, under suitable conditions, is able to achieve the objective of residual nudging for data assimilation problems with nonlinear observation operators. The 40-dimensional Lorenz-96 model is used to illustrate the performance of the iterative filter. Numerical results show that, while a normal EnKF may diverge with nonlinear observation operators, the proposed iterative filter remains stable and leads to reasonable estimation accuracy under various experimental settings.
Development and validation of a Kalman filter-based model for vehicle slip angle estimation
Gadola, M.; Chindamo, D.; Romano, M.; Padula, F.
2014-01-01
It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data.
Autonomous determination of orbit for probe around asteroids using unscented Kalman filter
Institute of Scientific and Technical Information of China (English)
崔平远; 崔祜涛; 黄翔宇; 栾恩杰
2003-01-01
The observed images of the asteroid and the asteroid reference images are used to obtain the probe-to-asteroid direction and the location of the limb features of the asteroid in the inertial coordinate. These informa-tion in combination with the shape model of the asteroid and attitude information of the probe are utilized to ob-tain the position of the probe. The position information is then input to the UKF which determines the real-timeorbit of the probe. Finally, the autonomous orbit determination algorithm is validated using digital simulation.The determination of orbit using UKF is compared with that using extended Kalman filter (EKF), and the resultshows that UKF is superior to EKF.
A Generalized Autocovariance Least-Squares Method for Kalman Filter Tuning
DEFF Research Database (Denmark)
Åkesson, Bernt Magnus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad
2008-01-01
This paper discusses a method for estimating noise covariances from process data. In linear stochastic state-space representations the true noise covariances are generally unknown in practical applications. Using estimated covariances a Kalman filter can be tuned in order to increase the accuracy...... of the state estimates. There is a linear relationship between covariances and autocovariance. Therefore, the covariance estimation problem can be stated as a least-squares problem, which can be solved as a symmetric semidefinite least-squares problem. This problem is convex and can be solved efficiently...... by interior-point methods. A numerical algorithm for solving the symmetric is able to handle systems with mutually correlated process noise and measurement noise. (c) 2007 Elsevier Ltd. All rights reserved....
Directory of Open Access Journals (Sweden)
Chen Siyu
2017-01-01
Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.
Directory of Open Access Journals (Sweden)
Doo Yong Choi
2016-04-01
Full Text Available Rapid detection of bursts and leaks in water distribution systems (WDSs can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA systems and the establishment of district meter areas (DMAs. Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.
A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM
Directory of Open Access Journals (Sweden)
Jianjun Ni
2014-01-01
Full Text Available Robot simultaneous localization and mapping (SLAM problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon
2013-10-01
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
Statistical analysis and Kalman filtering applied to nuclear materials accountancy
International Nuclear Information System (INIS)
Annibal, P.S.
1990-08-01
Much theoretical research has been carried out on the development of statistical methods for nuclear material accountancy. In practice, physical, financial and time constraints mean that the techniques must be adapted to give an optimal performance in plant conditions. This thesis aims to bridge the gap between theory and practice, to show the benefits to be gained from a knowledge of the facility operation. Four different aspects are considered; firstly, the use of redundant measurements to reduce the error on the estimate of the mass of heavy metal in an 'accountancy tank' is investigated. Secondly, an analysis of the calibration data for the same tank is presented, establishing bounds for the error and suggesting a means of reducing them. Thirdly, a plant-specific method of producing an optimal statistic from the input, output and inventory data, to help decide between 'material loss' and 'no loss' hypotheses, is developed and compared with existing general techniques. Finally, an application of the Kalman Filter to materials accountancy is developed, to demonstrate the advantages of state-estimation techniques. The results of the analyses and comparisons illustrate the importance of taking into account a complete and accurate knowledge of the plant operation, measurement system, and calibration methods, to derive meaningful results from statistical tests on materials accountancy data, and to give a better understanding of critical random and systematic error sources. The analyses were carried out on the head-end of the Fast Reactor Reprocessing Plant, where fuel from the prototype fast reactor is cut up and dissolved. However, the techniques described are general in their application. (author)
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.
2013-01-01
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
Extended Kalman filtering for continuous volumetric MR-temperature imaging.
Denis de Senneville, Baudouin; Roujol, Sébastien; Hey, Silke; Moonen, Chrit; Ries, Mario
2013-04-01
Real time magnetic resonance (MR) thermometry has evolved into the method of choice for the guidance of high-intensity focused ultrasound (HIFU) interventions. For this role, MR-thermometry should preferably have a high temporal and spatial resolution and allow observing the temperature over the entire targeted area and its vicinity with a high accuracy. In addition, the precision of real time MR-thermometry for therapy guidance is generally limited by the available signal-to-noise ratio (SNR) and the influence of physiological noise. MR-guided HIFU would benefit of the large coverage volumetric temperature maps, including characterization of volumetric heating trajectories as well as near- and far-field heating. In this paper, continuous volumetric MR-temperature monitoring was obtained as follows. The targeted area was continuously scanned during the heating process by a multi-slice sequence. Measured data and a priori knowledge of 3-D data derived from a forecast based on a physical model were combined using an extended Kalman filter (EKF). The proposed reconstruction improved the temperature measurement resolution and precision while maintaining guaranteed output accuracy. The method was evaluated experimentally ex vivo on a phantom, and in vivo on a porcine kidney, using HIFU heating. On the in vivo experiment, it allowed the reconstruction from a spatio-temporally under-sampled data set (with an update rate for each voxel of 1.143 s) to a 3-D dataset covering a field of view of 142.5×285×54 mm(3) with a voxel size of 3×3×6 mm(3) and a temporal resolution of 0.127 s. The method also provided noise reduction, while having a minimal impact on accuracy and latency.
ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER
Efendiev, Yalchin
2011-01-01
Reservoir data is usually scale dependent and exhibits multiscale features. In this paper we use the ensemble Kalman filter (EnKF) to integrate data at different spatial scales for estimating reservoir fine-scale characteristics. Relationships between the various scales is modeled via upscaling techniques. We propose two versions of the EnKF to assimilate the multiscale data, (i) where all the data are assimilated together and (ii) the data are assimilated sequentially in batches. Ensemble members obtained after assimilating one set of data are used as a prior to assimilate the next set of data. Both of these versions are easily implementable with any other upscaling which links the fine to the coarse scales. The numerical results with different methods are presented in a twin experiment setup using a two-dimensional, two-phase (oil and water) flow model. Results are shown with coarse-scale permeability and coarse-scale saturation data. They indicate that additional data provides better fine-scale estimates and fractional flow predictions. We observed that the two versions of the EnKF differed in their estimates when coarse-scale permeability is provided, whereas their results are similar when coarse-scale saturation is used. This behavior is thought to be due to the nonlinearity of the upscaling operator in the case of the former data. We also tested our procedures with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data yielded improved estimates. With better coarse-scale modeling and inversion techniques as more data at multiple coarse scales is made available, the proposed modification to the EnKF could be relevant in future studies.
Mobile location with NLOS identification and mitigation based on modified Kalman filtering.
Ke, Wei; Wu, Lenan
2011-01-01
In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications.
Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong
2017-08-01
Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.
Wang, Wei; Chen, Xiyuan
2018-02-23
In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.
Zhang, Qun; Yang, Yanfu; Xiang, Qian; Zhou, Zhongqing; Yao, Yong
2018-02-01
A joint compensation scheme based on cascaded Kalman filter is proposed, which can implement polarization tracking, channel equalization, frequency offset, and phase noise compensation simultaneously. The experimental results show that the proposed algorithm can not only compensate multiple channel impairments simultaneously but also improve the polarization tracking capacity and accelerate the convergence speed. The scheme has up to eight times faster convergence speed compared with radius-directed equalizer (RDE) + Max-FFT (maximum fast Fourier transform) + BPS (blind phase search) and can track up polarization rotation 60 times and 15 times faster than that of RDE + Max-FFT + BPS and CMMA (cascaded multimodulus algorithm) + Max-FFT + BPS, respectively.
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
International Nuclear Information System (INIS)
Tylee, J.L.
1980-01-01
A low-order, nonlinear model of the Loss-of-Fluid Test (LOFT) reactor plant, for use in Kalman filter estimators, is developed, described, and evaluated. This model consists of 31 differential equations and represents all major subsystems of both the primary and secondary sides of the LOFT plant. Comparisons between model calculations and available LOFT power range testing transients demonstrate the accuracy of the low-order model. The nonlinear model is numerically linearized for future implementation in Kalman filter and optimal control algorithms. The linearized model is shown to be an adequate representation of the nonlinear plant dynamics
The extended Kalman filter for forecast of algal bloom dynamics.
Mao, J Q; Lee, Joseph H W; Choi, K W
2009-09-01
A deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Deltat=1 day, 2h, 1h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 microg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r=0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r=0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the
Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives
DEFF Research Database (Denmark)
Lascu, Cristian; Jafarzadeh, Saeed; Fadali, M.Sami
2013-01-01
This paper investigates the application, design, and implementation of the square root unscented Kalman filter (UKF) (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UTs) in the prediction step in order to preserve the stochastic characteristics...... of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering to reduce computational errors. We discuss the theoretical aspects...
Directory of Open Access Journals (Sweden)
Dongyan Chen
2015-01-01
Full Text Available This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE. Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.
A new Approach for Kalman filtering on Mobile Robots in the presence of uncertainties
DEFF Research Database (Denmark)
Larsen, Thomas Dall; Andersen, Nils Axel; Ravn, Ole
1999-01-01
In many practical Kalman filter applications, the quantity of most significance for the estimation error is the process noise matrix. When filters are stabilized or performance is sought to be improved, tuning of this matrix is the most common method. This tuning process cannot be done before the...
DEFF Research Database (Denmark)
Høilund, Carsten; Moeslund, Thomas B.; Madsen, Claus B.
2010-01-01
This paper presents a method for determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing...
DEFF Research Database (Denmark)
Larsen, Thomas Dall; Bak, Martin; Andersen, Nils Axel
1998-01-01
A Kalman filter using encoder readings as inputs and vision measurements as observations is designed as a location estimator for an autonomously guided vehicle (AGV). To reduce the effect of modelling errors an augmented filter that estimates the true system parameters is designed. The traditional...
Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.
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.
International Nuclear Information System (INIS)
Shimazu, Y.; Rooijen, W.F.G. van
2014-01-01
Highlights: • Estimation of the reactivity of nuclear reactor based on neutron flux measurements. • Comparison of the traditional method, and the new approach based on Extended Kalman Filtering (EKF). • Estimation accuracy depends on filter parameters, the selection of which is described in this paper. • The EKF algorithm is preferred if the signal to noise ratio is low (low flux situation). • The accuracy of the EKF depends on the ratio of the filter coefficients. - Abstract: The Extended Kalman Filtering (EKF) technique has been applied for estimation of subcriticality with a good noise filtering and accuracy. The Inverse Point Kinetic (IPK) method has also been widely used for reactivity estimation. The important parameters for the EKF estimation are the process noise covariance, and the measurement noise covariance. However the optimal selection is quite difficult. On the other hand, there is only one parameter in the IPK method, namely the time constant for the first order delay filter. Thus, the selection of this parameter is quite easy. Thus, it is required to give certain idea for the selection of which method should be selected and how to select the required parameters. From this point of view, a qualitative performance comparison is carried out
Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen
2014-05-09
Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed
An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data
Ghorbani, Esmaeil; Cha, Young-Jin
2018-04-01
Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom Bouc-Wen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.
Application of wavelet-based multi-model Kalman filters to real-time flood forecasting
Chou, Chien-Ming; Wang, Ru-Yih
2004-04-01
This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.
Assimilation of lake water surface temperature observations using an extended Kalman filter
Directory of Open Access Journals (Sweden)
Ekaterina Kourzeneva
2014-10-01
Full Text Available A new extended Kalman filter (EKF-based algorithm to assimilate lake water surface temperature (LWST observations into the lake model/parameterisation scheme Freshwater Lake (FLake has been developed. The data assimilation algorithm has been implemented into the stand-alone offline version of FLake. The mixed and non-mixed regimes in lakes are treated separately by the EKF algorithm. The timing of the ice period is indicated implicitly: no ice if water surface temperature is measured. Numerical experiments are performed using operational in-situ observations for 27 lakes and merged observations (in-situ plus satellite for 4 lakes in Finland. Experiments are analysed, potential problems are discussed, and the role of early spring observations is studied. In general, results of experiments are promising: (1 the impact of observations (calculated as the normalised reduction of the LWST root mean square error comparing to the free model run is more than 90% and (2 in cross-validation (when observations are partly assimilated, partly used for validation the normalised reduction of the LWST error standard deviation is more than 65%. The new data assimilation algorithm will allow prognostic variables in the lake parameterisation scheme to be initialised in operational numerical weather prediction models and the effects of model errors to be corrected by using LWST observations.
Noninvasive estimation of global activation sequence using the extended Kalman filter.
Liu, Chenguang; He, Bin
2011-03-01
A new algorithm for 3-D imaging of the activation sequence from noninvasive body surface potentials is proposed. After formulating the nonlinear relationship between the 3-D activation sequence and the body surface recordings during activation, the extended Kalman filter (EKF) is utilized to estimate the activation sequence in a recursive way. The state vector containing the activation sequence is optimized during iteration by updating the error variance/covariance matrix. A new regularization scheme is incorporated into the "predict" procedure of EKF to tackle the ill-posedness of the inverse problem. The EKF-based algorithm shows good performance in simulation under single-site pacing. Between the estimated activation sequences and true values, the average correlation coefficient (CC) is 0.95, and the relative error (RE) is 0.13. The average localization error (LE) when localizing the pacing site is 3.0 mm. Good results are also obtained under dual-site pacing (CC = 0.93, RE = 0.16, and LE = 4.3 mm). Furthermore, the algorithm shows robustness to noise. The present promising results demonstrate that the proposed EKF-based inverse approach can noninvasively estimate the 3-D activation sequence with good accuracy and the new algorithm shows good features due to the application of EKF.
Extended Kalman filtering for the detection of damage in linear mechanical structures
Liu, X.; Escamilla-Ambrosio, P. J.; Lieven, N. A. J.
2009-09-01
This paper addresses the problem of assessing the location and extent of damage in a vibrating structure by means of vibration measurements. Frequency domain identification methods (e.g. finite element model updating) have been widely used in this area while time domain methods such as the extended Kalman filter (EKF) method, are more sparsely represented. The difficulty of applying EKF in mechanical system damage identification and localisation lies in: the high computational cost, the dependence of estimation results on the initial estimation error covariance matrix P(0), the initial value of parameters to be estimated, and on the statistics of measurement noise R and process noise Q. To resolve these problems in the EKF, a multiple model adaptive estimator consisting of a bank of EKF in modal domain was designed, each filter in the bank is based on different P(0). The algorithm was iterated by using the weighted global iteration method. A fuzzy logic model was incorporated in each filter to estimate the variance of the measurement noise R. The application of the method is illustrated by simulated and real examples.
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.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei
2018-02-01
Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.
A balanced Kalman filter ocean data assimilation system with application to the South Australian Sea
Li, Yi; Toumi, Ralf
2017-08-01
In this paper, an Ensemble Kalman Filter (EnKF) based regional ocean data assimilation system has been developed and applied to the South Australian Sea. This system consists of the data assimilation algorithm provided by the NCAR Data Assimilation Research Testbed (DART) and the Regional Ocean Modelling System (ROMS). We describe the first implementation of the physical balance operator (temperature-salinity, hydrostatic and geostrophic balance) to DART, to reduce the spurious waves which may be introduced during the data assimilation process. The effect of the balance operator is validated in both an idealised shallow water model and the ROMS model real case study. In the shallow water model, the geostrophic balance operator eliminates spurious ageostrophic waves and produces a better sea surface height (SSH) and velocity analysis and forecast. Its impact increases as the sea surface height and wind stress increase. In the real case, satellite-observed sea surface temperature (SST) and SSH are assimilated in the South Australian Sea with 50 ensembles using the Ensemble Adjustment Kalman Filter (EAKF). Assimilating SSH and SST enhances the estimation of SSH and SST in the entire domain, respectively. Assimilation with the balance operator produces a more realistic simulation of surface currents and subsurface temperature profile. The best improvement is obtained when only SSH is assimilated with the balance operator. A case study with a storm suggests that the benefit of the balance operator is of particular importance under high wind stress conditions. Implementing the balance operator could be a general benefit to ocean data assimilation systems.
Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter.
Li, Liangping; Zhang, Meijing; Katzenstein, Kurt
2017-11-01
The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time-lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real-time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation. © 2017, National Ground Water Association.
Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java
Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri
2018-03-01
Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.
State and force observers based on multibody models and the indirect Kalman filter
Sanjurjo, Emilio; Dopico, Daniel; Luaces, Alberto; Naya, Miguel Ángel
2018-06-01
The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibody-based observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB® toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab.
Event-triggered Kalman-consensus filter for two-target tracking sensor networks.
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.
Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst
2016-05-01
Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method
Institute of Scientific and Technical Information of China (English)
杨海; 李威; 罗成名
2015-01-01
Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
A Hybrid Extended Kalman Filter as an Observer for a Pot-Electro-Magnetic Actuator
International Nuclear Information System (INIS)
Schmidt, Simon; Mercorelli, Paolo
2017-01-01
This paper deals with an application in which a hybrid extended Kalman Filter (HEKF) is used to estimate state variables in a U-shaped electro-magnetic actuator to be used in mechanical systems. In this context a hybrid Kalman Filter is the one which switches between different models. The paper proposes a hybrid model for an extended Kalman Filter to be used as an observer to estimate the state and to control the force of the actuator. Applications include position, velocity and force control in automotive, engine and manufacturing systems. This work is focused on the estimation of state variables of the actuator. Simulated results show the effectiveness of the proposed approach. (paper)
Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
Directory of Open Access Journals (Sweden)
Qingli Li
2015-01-01
Full Text Available To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.
Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica
2009-01-01
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
Directory of Open Access Journals (Sweden)
Milad eLankarany
2013-09-01
Full Text Available Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012 with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.
Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter
Directory of Open Access Journals (Sweden)
Pavel Brandstetter
2013-01-01
Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.
Application Of Kalman Filter In Navigation Process Of Automated Guided Vehicles
Directory of Open Access Journals (Sweden)
Śmieszek Mirosław
2015-09-01
Full Text Available In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.
A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)
Truong, S. H.
1999-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.
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.
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.
Demodulation of moire fringes in digital holographic interferometry using an extended Kalman filter.
Ramaiah, Jagadesh; Rastogi, Pramod; Rajshekhar, Gannavarpu
2018-03-10
This paper presents a method for extracting multiple phases from a single moire fringe pattern in digital holographic interferometry. The method relies on component separation using singular value decomposition and an extended Kalman filter for demodulating the moire fringes. The Kalman filter is applied by modeling the interference field locally as a multi-component polynomial phase signal and extracting the associated multiple polynomial coefficients using the state space approach. In addition to phase, the corresponding multiple phase derivatives can be simultaneously extracted using the proposed method. The applicability of the proposed method is demonstrated using simulation and experimental results.
Park, K. C.; Belvin, W. Keith
1990-01-01
A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.
Use of Kalman filter methods in analysis of in-pile LMFBR accident simulations
International Nuclear Information System (INIS)
Meek, C.C.; Doerner, R.C.
1983-01-01
Kalman filter methodology has been applied to inpile liquid-metal fast breeder reactor simulation experiments to obtain estimates of the fuel-clad thermal gap conductance. A transient lumped parameter model of the experiment is developed. An optimal estimate of the state vector chosen to characterize the experiment is obtained through the use of the Kalman filter. From this estimate, the fuel-clad thermal gap conductance is calculated as a function of time into the test and axial position along the length of the fuel pin
DEFF Research Database (Denmark)
Pierobon, Leonardo; Schlanbusch, Rune; Kandepu, Rambabu
2014-01-01
for this project. Considering the plant dynamics, it is of paramount importance to monitor the peak temperatures within the once-through boiler serving the bottoming unit to prevent the decomposition of the working fluid. This paper accordingly aims at applying the unscented Kalman filter to estimate...... the temperature distribution inside the primary heat exchanger by engaging a detailed and distributed model of the system and available measurements. Simulation results prove the robustness of the unscented Kalman filter with respect to process noise, measurement disturbances and initial conditions....
Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim
2016-01-01
Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.
Sana, Furrukh
2016-02-23
Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.
Directory of Open Access Journals (Sweden)
Tamer Mekky Ahmed Habib
2014-06-01
Full Text Available The main goal of this research is to establish spacecraft orbit and attitude control algorithms based on extended Kalman filter which provides estimates of spacecraft orbital and attitude states. The control and estimation algorithms must be capable of dealing with the spacecraft conditions during the detumbling and attitude acquisition modes of operation. These conditions are characterized by nonlinearities represented by large initial attitude angles, large initial angular velocities, large initial attitude estimation error, and large initial position estimation error. All of the developed estimation and control algorithms are suitable for application to the next Egyptian scientific satellite, EGYPTSAT-2. The parameters of the case-study spacecraft are similar but not identical to the former Egyptian satellite EGYPTSAT-1. This is done because the parameters of EGYPTSAT-2 satellite have not been consolidated yet. The sensors utilized are gyro, magnetometer, and GPS. Gyro and magnetometer are utilized to provide measurements for the estimates of spacecraft attitude state vector where as magnetometer and GPS are utilized to provide measurements for the estimates of spacecraft orbital state vector.
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.
Directory of Open Access Journals (Sweden)
Rita Fontanella
2018-05-01
Full Text Available This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.
Fontanella, Rita; Accardo, Domenico; Moriello, Rosario Schiano Lo; Angrisani, Leopoldo; Simone, Domenico De
2018-05-07
This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.
Directory of Open Access Journals (Sweden)
Lijun Song
2018-01-01
Full Text Available The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.
Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks
Institute of Scientific and Technical Information of China (English)
ZHANG Peng; QI Wen-Juan; DENG Zi-Li
2014-01-01
This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.
Directory of Open Access Journals (Sweden)
Suleiman M. Sharkh
2012-04-01
Full Text Available A robust extended Kalman filter (EKF is proposed as a method for estimation of the state of charge (SOC of lithium-ion batteries used in hybrid electric vehicles (HEVs. An equivalent circuit model of the battery, including its electromotive force (EMF hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
Institute of Scientific and Technical Information of China (English)
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
A Kalman Filter for SINS Self-Alignment Based on Vector Observation.
Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu
2017-01-29
In this paper, a self-alignment method for strapdown inertial navigation systems based on the q -method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.
A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.
Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing
2018-03-07
The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.
A Two-stage Kalman Filter for Sensorless Direct Torque Controlled PM Synchronous Motor Drive
Directory of Open Access Journals (Sweden)
Boyu Yi
2013-01-01
Full Text Available This paper presents an optimal two-stage extended Kalman filter (OTSEKF for closed-loop flux, torque, and speed estimation of a permanent magnet synchronous motor (PMSM to achieve sensorless DTC-SVPWM operation of drive system. The novel observer is obtained by using the same transformation as in a linear Kalman observer, which is proposed by C.-S. Hsieh and F.-C. Chen in 1999. The OTSEKF is an effective implementation of the extended Kalman filter (EKF and provides a recursive optimum state estimation for PMSMs using terminal signals that may be polluted by noise. Compared to a conventional EKF, the OTSEKF reduces the number of arithmetic operations. Simulation and experimental results verify the effectiveness of the proposed OTSEKF observer for DTC of PMSMs.
Real-time identification of the resistive-wall-mode in DIII-D with Kalman filter ELM discrimination
International Nuclear Information System (INIS)
Edgell, D.H.; Fransson, C.M.; Humphreys, D.A.; Ferron, J.R.; Garofalo, A.M.; Kim, J.S.; La Haye, R.J.; Okabayashi, M.; Reimerdes, H.; Strait, E.J.; Turnbull, A.D.
2004-01-01
The resistive-wall-mode (RWM) is a major performance-limiting instability in present-day tokamaks. Active control and stabilization of the mode will almost certainly be essential for the success of advanced tokamaks and for the economic viability of tokamak fusion reactors. High performance tokamak plasmas often experience edge-localized-modes (ELMs) which can interfere with RWM identification and control. If the RWM control scheme reacts to an ELM the RWM may be driven unstable instead of controlled. An algorithm for real-time identification of the RWM with discrimination of ELMs in the DIII-D tokamak has been developed using a combination of matched filter and Kalman filter methods. The algorithm has been implemented in DIII-D's real-time plasma control system (PCS) and is available to drive active mode control schemes