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 literatu...
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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 matrice...
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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 K...
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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 adapt...
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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 f...
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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 n...
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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.
The aim of this paper is to present a software framework facilitating implementation, testing and use of various nonlinear estimation methods. This framework is designed to offer an easy to use tool for state estimati...
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The aim of this paper is to present a software framework facilitating implementation, testing and use of various nonlinear estimation methods. This framework is designed to offer an easy to use tool for state estimation of discrete time dynamic stochastic systems. Besides implementation of various local and global state estimation methods it contains procedures for system design and simulation. Its strength is in the fact that it provides means that help students get acquainted with nonlinear state estimation problem and to be able to test features of various estimation methods. Another considerable advantage of proposed framework is its high modularity and extensibility. The paper briefly describes nonlinear estimation problem and its general solution using the Bayesian approach leading to the Bayesian recursive relations. Then it presents key features of the software framework designed in MATLAB environment that supports straightforward implementation of estimation methods based on the Bayesian approach. The strengths of the framework are demonstrated on implementation of the Divided difference filter 1st order.
A suboptimal dual controller for discrete stochastic systems with unknown parameters based on the bicriterial approach is proposed and discussed. It is supposed that all the random quantities are non-Gaussian. This as...
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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 u...
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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.
Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modifica- tion of the Unscented Kalman Filter is derived and it is used in the G...
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