Nonlinear state estimation by evolution strategies based gaussian sum particle filter

Authors:
Katsuji Uosaki;Toshiharu Hatanaka
Affiliations:
Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan;Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

Citing 2
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Abstract

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a novel particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the posteriori distribution and Evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.