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 ...
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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...
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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 probabi...
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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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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. Bec...
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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 c...
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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.
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 whol...
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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.
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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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.
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