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15th IFAC Symposium on System Identification, SYSID 2009

作者： Straka, Ondšej Šimandl, Miroslav Department of Cybernetics and Research Centre: Data - Algorithms - Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Plzeň Czech Republic

ISBN: (纸本)9783902661470

The paper deals with the particle filter in discrete-time nonlinear non-Gaussian system state estimation. One of the key parameters affecting estimate quality of the particle filter is the sample size. In the literature, there is a number of techniques coming from various ideas that aim at adapting the sample size while keeping quality in some sense fixed. The goal of the paper is to provide a survey of sample size adaptation techniques, to classify them and to discuss various aspects concerning the techniques. © 2009 IFAC.

关键词： State estimation

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Methods for estimating state and measurement noise covariance matrices: Aspects and comparison

Methods for estimating state and measurement noise covarianc...

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作者： Duník, Jindšich Šimandl, Miroslav Straka, Ondšej Department of Cybernetics and Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9783902661470

The paper deals with estimation of noise covariance matrices in state and measurement equations of linear discrete-time stochastic dynamic systems. In the last decade several novel methods for noise covariance matrices estimation, which are based on state estimation techniques, have been proposed. Unfortunately, the novel methods have been compared mainly with classical methods proposed in the seventies only. The aim of the paper is to analyse identifiability of state noise parameters by means of the Bayesian approach and to summarise and compare the novel methods from both theoretical and numerical point of view. © 2009 IFAC.

关键词： State estimation

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A software framework and tool for nonlinear state estimation

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IFAC Proceedings Volumes 2009年第10期42卷 510-515页

作者： Ondřej Straka Miroslav Flídr Jindřich Duník Miroslav Ŝimandl Department of Cybernetics & Research Centre Data - Algorithms - Decision Making University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are presented in two examples.

关键词： Nonlinear state and parameter estimation Bayesian approach Software framework

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Methods for Estimating State and Measurement Noise Covariance Matrices: Aspects and Comparison

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IFAC Proceedings Volumes 2009年第10期42卷 372-377页

作者： Jindřich Duník Miroslav Ŝimandl Ondřej Straka Department of Cybernetics & Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

关键词： State estimation Estimation theory Covariance estimation Kalman filtering

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A Survey of Sample Size Adaptation Techniques for Particle Filters

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IFAC Proceedings Volumes 2009年第10期42卷 1358-1363页

作者： Ondřej Straka Miroslav Ŝimandl Department of Cybernetics and Research Centre: Data - Algorithms - Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Plzeň Czech Republic

关键词： State estimation Particle filter Sample size Adaptation

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Gaussian mixtures proposal density in particle filter for track-before-detect

Gaussian mixtures proposal density in particle filter for tr...

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International Conference on Information Fusion

作者： Ondrej Straka Miroslav Simandl Jindrich Dunik Department of Cybernetics Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Czech Republic

The paper deals with state estimation for the track-before-detect approach using the particle filter. The focus is aimed at the track initiation proposal density of the particle filter which considerably affects estimate quality. The goal of the paper is to design a proposal based on a Gaussian mixture using a bank of extended Kalman filters. This leads to root mean square error lower than that achieved by usual simple track initiation proposals. Due to application of several developed techniques reducing computational requirements of the designed proposal, the Gaussian mixture particle filter also achieves lower computational requirements than ordinary particle filter. Performance of the proposed Gaussian mixture track initiation proposal in the particle filter is demonstrated in a numerical example.

关键词： Proposals Particle filters Particle tracking State estimation Target tracking Filtering Sampling methods Particle measurements Position measurement Signal processing algorithms

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Multistage prediction error adaptive dual controller

Multistage prediction error adaptive dual controller

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11th IASTED International Conference on Intelligent Systems and Control, ISC 2008

作者： Flídr, Miroslav Šimandl, Miroslav Král, Ladislav Research Centre Data - Algorithms - Decision Making University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic Department of Cybernetics Faculty of Applied Sciences University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic

ISBN: (纸本)9780889867741

The problem of optimal stochastic control of discrete stochastic state space system with uncertain parameters is considered. This problem is analytically unsolvable even for simple systems. A suboptimal dual control approach is presented which makes it possible to solve the multistage optimisation problem analytically. The analytical solvability is guaranteed by employing the partial certainty equivalence principle where the certainty equivalence assumption is enforced only on part of the augmented state. The dual properties of the resulting control are then ensured by a modification of the criterion which evaluates quality of estimates using weighted prediction errors. The proposed dual controller is compared to other non-dual controllers in a numerical example.

关键词： Stochastic systems

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Estimation of State and Measurement Noise Covariance Matrices by Multi-Step Prediction

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IFAC Proceedings Volumes 2008年第2期41卷 3689-3694页

作者： Jindřich Duník Miroslav Šimandl Department of Cybernetics & Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

Estimation of noise covariance matrices for linear or nonlinear stochastic dynamic systems is treated. The novel off-line technique for estimation of the covariance matrices of the state and measurement noises is designed. The technique is based on the multi-step prediction error and on knowledge of the system initial condition and it takes an advantage of the well-known standard relations from the area of state estimation techniques and least square method. The theoretical results are illustrated in numerical examples.

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Application of Estimation Techniques on Queue Lengths Estimation in Traffic Network

Application of Estimation Techniques on Queue Lengths Estima...

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IEEE International Conference on Cybernetic Intelligent Systems

作者： Pavla Pecherkova Miroslav Flidr Jindrich Dunik Research Centre Data - Algorithms - Decision Making Department of Adaptive Systems Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic Research Centre Data - Algorithms - Decision Making Department of Cybernetics Faculty of Applied Sciences University of West Bohemia

ISBN: (纸本)9781424429141

The paper deals with the application of the state and parameter estimation techniques in the area of traffic control. The most important properties of the traffic system are described and the model of the traffic system, based on the traffic flow conservation principle, is presented. Various estimation and identification techniques are briefly introduced and applied for three types of roads and micro-regions, namely for city ring road, peripheral road, and city centre. Performance of estimation techniques is validated, using the derived models on real and synthetic data coming from Prague, with respect to accuracy and complexity.

关键词： Traffic system model Stochastic systems State estimation Kalman filtering Estimation Techniques traffic systems stochastic systems Kalman filtering State estimation traffic network Road type Prague Czech Republic synthetic data Queue

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Adaptive particle filter with fixed empirical density quality

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IFAC Proceedings Volumes 2008年第2期41卷 6484-6489页

作者： Ondřej Straka Miroslav Šimandl Department of Cybernetics and Research Centre: Data - Algorithms - Decision Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Plzeň Czech Republic

The paper deals with the particle filter in state estimation of a discrete-time nonlinear non-Gaussian system. The goal of the paper is to design a sample size adaptation technique to guarantee the quality of an empirical probability density function (pdf) which approximates a target filtering pdf. The quality is measured by inaccuracy (cross-information) between the empirical pdf and the filtering pdf. It is shown that for increasing sample size the inaccuracy converges to the Shannon differential entropy (SDE) of the filtering pdf. The proposed technique adapts the sample size to keep a difference between the inaccuracy and the SDE within prespecified bounds with a pre-specified probability. The particle filter with the proposed sample size adaptation technique is illustrated in a numerical example.

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