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International Conference on Information Fusion

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

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.

关键词： Entropy Estimation Particle measurements Atmospheric measurements Density measurement Approximation methods Covariance matrix

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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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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

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.

关键词： Approximation methods Covariance matrix State estimation Kalman filters Measurement Noise

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Truncated unscented particle filter

Truncated unscented particle filter

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American Control Conference (ACC)

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.

关键词： Covariance matrix Approximation methods Vehicles State estimation Roads Monte Carlo methods Kalman filters

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Dual Adaptive Controllers based on Partial Certainty Equivalence *

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IFAC Proceedings Volumes 2011年第1期44卷 3457-3462页

作者： Miroslav Flídr Miroslav Šimandl 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

Abstract 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.

关键词： Stochastic adaptive control Dual control Cautious control Partial certainty equivalence

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Adaptive choice of scaling parameter in derivative-free local filters

Adaptive choice of scaling parameter in derivative-free loca...

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

ISBN: (纸本)9780982443811

The paper deals with adaptive choice of the scaling parameter in derivative-free local filters. In the last decade several novel local derivative-free filtering methods have been proposed. These methods exploiting Stirling's interpolation and the unscented transformation are, however, conditioned by specification of a scaling parameter significantly influencing the quality of the state estimate. Surprisingly, almost no attention has been devoted to a suitable choice of the parameter. In fact, only a few basic recommendations have been provided, which are rather general and do not respect the particular system description. The choice of the parameter thus remains mainly on a user. The goal of the paper is to provide a technique for adaptive choice of the scaling parameter of the derivative-free local filters.

关键词： Nonlinear filtering

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Non-myopic innovations dual controller

Non-myopic innovations dual controller

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10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2010

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)9783902661852

Optimal control of a linear discrete stochastic state space system with uncertain parameters is treated. The problem statement leads to design of a dual controllers. Unfortunately, except for few special cases it is not possible to obtain closed-form solution for such controller. Many suboptimal approaches were proposed to overcome this obstacle, however, mostly they restrict the control horizon only to one step ahead and thus suffer from myopic behaviour. This paper presents technique that makes it possible to derive suboptimal dual controller in closed-form for arbitrarily long control horizon. The design of the presented controller is based on the innovations dual controller and use of the partial certainty equivalence principle. The proposed dual controller is compared to other non-dual controllers in a numerical example. © 2010 IFAC.

关键词： Controllers

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Adaptive choice of scaling parameter in derivative-free local filters

Adaptive choice of scaling parameter in derivative-free loca...

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International Conference on Information Fusion

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

关键词： Covariance matrix Interpolation Kalman filters Prediction algorithms State estimation Mathematical model

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Multiple-model filtering with multiple constraints

Multiple-model filtering with multiple constraints

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American Control Conference (ACC)

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

The paper deals with state estimation of nonlinear stochastic systems, where the state is subject to nonlinear equality constraints reflecting some physical or technological limitations. Usually, this problem of constrained state estimation is solved within the Kalman filtering framework. The goal of the paper is to provide a generalization of the solution to a multiplemodel multiple-constraint problem, where the two-step method for constraint application is adopted. In addition, the model weight computation is analyzed and a weight correction for the constrained estimation is proposed. The proposed method is illustrated in a numerical example.

关键词： Filtering State estimation Transforms Target tracking Nonlinear control systems Control systems Stochastic systems Paper technology Kalman filters Computational modeling

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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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2009 12th International Conference on Information Fusion, FUSION 2009

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

ISBN: (纸本)9780982443804

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 qual-ity. 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 com-putational requirements than ordinary particle filter. Per-formance of the proposed Gaussian mixture track initiation proposal in the particle filter is demonstrated in a numerical example. ©2009 ISIF.

关键词： Nonlinear filtering

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A software framework and tool for nonlinear state estimation

A software framework and tool for nonlinear state estimation

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15th IFAC Symposium on System Identification, SYSID 2009

作者： Straka, Ondšej Flídr, Miroslav Duník, Jindšich Šimandl, Miroslav Department of Cybernetics and Research Centre Data - Algorithms - Decision Making University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic

ISBN: (纸本)9783902661470

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. © 2009 IFAC.

关键词： Nonlinear analysis

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