Adaptive MCMC particle filter for nonlinear and non-Gaussian state estimation - 文章详情页

作者：

Pei, Fujun (Pei, Fujun.) | Cui, Pingyuan (Cui, Pingyuan.) | Chen, Yangzhou (Chen, Yangzhou.) (学者：陈阳舟)

收录：

EI Scopus

摘要：

The　particle　filter　is　well　known　as　a　state　estimation　method　for　nonlinear　and　non-Gaussian　system.　However,　particle　filter　has　the　inherent　drawbacks　such　as　samples　less　of　diversity　and　the　computational　complexity　depends　on　the　number　of　samples　used　for　state　estimation　process.　In　this　paper,　the　adaptive　Markov　chain　Monte　Carlo　(MCMC)　particle　filter　is　proposed　in　order　to　overcome　these　drawbacks.　In　the　new　algorithm,　the　KLD-sampling　and　MCMC　sampling　are　simultaneously　used　to　improve　the　performance　of　particle　filter.　The　computer　simulations　are　performed　to　compare　the　adaptive　MCMC　particle　filter　algorithm,　the　MCMC　particle　filter　and　particle　filter　in　performance.　The　simulation　results　demonstrated　that　the　adaptive　MCMC　particle　filter　is　very　efficient　and　smaller　time　consumption　compared　to　MCMC　particle　filter　and　particle　filter.　Therefore,　the　MCMC　adaptive　particle　is　more　suitable　to　the　nonlinear　and　non-　Gaussian　state　estimation.　©　2008　IEEE.

关键词：

Adaptive filtering Gaussian distribution Gaussian noise (electronic) Markov chains Monte Carlo methods Nonlinear analysis Sampling State estimation

作者机构：

[ 1 ] [Pei, Fujun]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Cui, Pingyuan]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
[ 3 ] [Chen, Yangzhou]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China

通讯作者信息：

电子邮件地址：

pfj@bjut.edu.cn

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