The particle filter for nonlinear state estimation of discrete time dynamic stochastic systems is treated. The functional sampling density of the particle filter strongly affecting estimate quality is studied. The den...
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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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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 assumption induces that a global estimation method has to be used. The Gaussian sum method with multiple linearization technique was chosen and applied in the bicriterial control approach. The probing part of the control law is determined for each local node of estimated probability density function separately and respects accuracy of each local estimate inherent in the estimated probability density function. A comparison of the proposed modified bicriterial controller and the bicriterial controller which uses global point estimate only is shown in some numerical examples.
Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modification of the Unscented Kalman Filter is derived and it is used in the Gau...
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Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modification of the Unscented Kalman Filter is derived and it is used in the Gaussian Sum Filter framework. The new Sigma Point Gaussian Sum Filter is designed and some aspects of the filter are presented. Estimation quality and computational demands of the filter are illustrated in a numerical example.
The particle filter for nonlinear state estimation of discrete time dynamic stochastic systems is treated. The functional sampling density of the particle filter strongly affecting estimate quality is studied. The den...
详细信息
The particle filter for nonlinear state estimation of discrete time dynamic stochastic systems is treated. The functional sampling density of the particle filter strongly affecting estimate quality is studied. The density is given by weighted mixture of the transition probability density functions. The weights are calculated using distance of two reference variable probability density functions representing prior and measurement information. The aim is to find a suitable distance that does not suffer from problems with its numerical computation and that can be computed for a large set of systems analytically. It seems that the Bhattacharyya distance is feasible for evaluation of such a distance. Quality and computational demands of the functional particle filter with primary weights computed using the Bhattacharyya distance are illustrated in a numerical example.
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