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作者： Straka, Ondřej Duník, Jindřich Šimandl, Miroslav Research Centre Data-Algorithms-Decision Making Department of Cybernetics University of West Bohemia Pilsen Czech Republic

ISBN: (纸本)9781457700804

The problem of state estimation of nonlinear stochastic dynamic systems with nonlinear inequality constraints is treated. The paper focuses on a particle filtering approach, which provides an estimate of the state in the form of a probability density function. A new computationally efficient particle filter for the constrained estimation problem is proposed. The importance function of the particle filter is generated by the unscented Kalman filter that is supplemented with a designed truncation technique to accommodate the constraint. The proposed filter is illustrated in a numerical example. © 2011 AACC American Automatic Control Council.

关键词： State estimation

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Particle based probability density fusion with differential Shannon entropy criterion

Particle based probability density fusion with differential ...

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作者： Ajgl, Jiří Šimandl, Miroslav Department of Cybernetics and Research Centre Data - Algorithms - Decision Making Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

ISBN: (纸本)9781457702679

This paper focuses on a decentralised nonlinear estimation problem in a multiple sensor network. The stress is laid on the optimal fusion of probability densities conditioned by different data. The probability density conditioned by the common data is supposed to be unavailable. The optimal fusion is elaborated in the particle filtering and differential Shannon entropy framework. The conversion of weighted particles into a continuous probability density function is performed implicitly by the time update. Further, the issue of sampling density proposal is explored. The proposed approach is illustrated in numerical examples. © 2011 IEEE.

关键词： Monte Carlo methods

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An efficient constrained Gaussian particle filter

An efficient constrained Gaussian particle filter

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作者： Straka, Ondřej Šimandl, Miroslav 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)9783902661937

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the local filters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering framework, there are several approaches to the constrained state estimation, mostly based on discarding samples violating the constraint with a possible increase of their number to improve the estimate quality. The paper aims at proposing a modification to an importance function of the particle filter in order to increase efficiency of sampling while keeping the computational complexity low. The proposed modification is utilized within the Gaussian particle filter which is advantageous for its low computational complexity. Complexity and estimation quality of the proposed constrained Gaussian particle filter is compared to other constrained particle filters in a numerical example. © 2011 IFAC.

关键词： Stochastic systems

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The development of a randomised unscented Kalman filter

The development of a randomised unscented Kalman filter

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作者： Duník, Jindřich Straka, Ondřej Šimandl, Miroslav 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)9783902661937

The paper deals with state estimation of nonlinear stochastic dynamic systems. Traditional filters providing local estimates of the states, such as the extended Kalman filter, unscented Kalman filter or the cubature Kalman filter, are based on approximations which lead to biased estimates of the state and measurement statistics. The aim of the paper is to propose a new local filter that utilises a randomised unscented transformation which is a special case of stochastic integration rules providing an unbiased estimate of an integral. The new filter provides estimates of higher quality than the traditional filters and renders a randomised version of the unscented Kalman filter. The proposed filter is illustrated in a numerical example. © 2011 IFAC.

关键词： State estimation

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Performance evaluation of local state estimation methods in bearings-only tracking problems

Performance evaluation of local state estimation methods in ...

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作者： Straka, Ondřej Duník, Jindřich Simandl, Miroslav Department of Cybernetics Research Centre Data-Algorithms-Decision Making Faculty of Applied Sciences Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9781457702679

The paper deals with a performance analysis of several local filters within three bearing-only tracking scenarios. Performance of the extended Kalman filter, unscented Kalman filter, unscented Kalman filter with adaptive scaling parameter, which represent generic filters, and the shifted Rayleigh filter, which is designed solely for the bearing-only tracking problem, is compared using the root mean square error, averaged normalized estimation error squared and non-credibility index. The simulations show that the unscented Kalman filter with adaptive scaling parameter achieves similar or even better performance than the shifted Rayleigh filter. © 2011 IEEE.

关键词： Nonlinear filtering

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Gaussian sum unscented Kalman filter with adaptive scaling parameters

Gaussian sum unscented Kalman filter with adaptive scaling p...

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作者： Straka, Ondřej Duník, Jindřich Šimandl, Miroslav Department of Cybernetics Faculty of Applied Sciences Research Centre Data-Algorithms-Decision Making Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9781457702679

The paper deals with state estimation of nonlinear non-Gaussian discrete dynamic systems by a bank of unscented Kalman filters. The stress is laid on an adaptive choice of a scaling parameter of the unscented Kalman filters to increase estimate quality over the standard Gaussian sum unscented Kalman filter. Several optimization criteria for adapting the scaling parameter are proposed and discussed and to apply the scaling parameter adaptation within the Gaussian sum framework, three adaptation procedures are proposed. Performance of the proposed estimation methods is analyzed through the root mean square error and non-credibility index in a numerical example. © 2011 IEEE.

关键词： State estimation

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Dual adaptive controllers based on partial certainty equivalence

Dual adaptive controllers based on partial certainty equival...

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作者： Flídr, Miroslav Šimandl, Miroslav 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)9783902661937

The article deals with the optimal control of a linear discrete stochastic state space system with uncertain parameters. The solution of this optimization problem leads to design of controllers with dual features. Because the closed-form solution is hardly attainable a suitable suboptimal approaches has to be used. Many of the simpler approaches restrict the control horizon only to one step ahead and thus suffer from the myopic behavior. One way how to overcome this restriction is to use the partial certainty equivalence approximation of the joint probability density functions. The goal of this paper is to present a comparison of suboptimal adaptive dual control methods employing this approximation. © 2011 IFAC.

关键词： Controllers

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The Development of a Randomised Unscented Kalman Filter

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IFAC Proceedings Volumes 2011年第1期44卷 8-13页

作者： Jindřich Duník Ondřej Straka 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

Abstract The paper deals with state estimation of nonlinear stochastic dynamic systems. Traditional filters providing local estimates of the states, such as the extended Kalman filter, unscented Kalman filter or the cubature Kalman filter, are based on approximations which lead to biased estimates of the state and measurement statistics. The aim of the paper is to propose a new local filter that utilises a randomised unscented transformation which is a special case of stochastic integration rules providing an unbiased estimate of an integral. The new filter provides estimates of higher quality than the traditional filters and renders a randomised version of the unscented Kalman filter. The proposed filter is illustrated in a numerical example.

关键词： State estimation Estimation theory Kalman filtering

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An Efficient Constrained Gaussian Particle Filter

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IFAC Proceedings Volumes 2011年第1期44卷 11973-11978页

作者： Ondřej Straka 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

Abstract The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the local filters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering framework, there are several approaches to the constrained state estimation, mostly based on discarding samples violating the constraint with a possible increase of their number to improve the estimate quality. The paper aims at proposing a modification to an importance function of the particle filter in order to increase efficiency of sampling while keeping the computational complexity low. The proposed modification is utilized within the Gaussian particle filter which is advantageous for its low computational complexity. Complexity and estimation quality of the proposed constrained Gaussian particle filter is compared to other constrained particle filters in a numerical example.

关键词： Constrained state estimation Estimation theory Particle filtering Stochastic systems Nonlinear systems

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Gaussian sum unscented Kalman filter with adaptive scaling parameters

Gaussian sum unscented Kalman filter with adaptive scaling p...

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International Conference on Information Fusion

作者： Ondřej Straka Jindřich Duník Miroslav Šimandl Research Centre Data-Algorithms-Decision Making Department of Cybernetics Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

关键词： Covariance matrix Kalman filters Approximation methods State estimation Noise Prediction algorithms Optimization

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