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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:XiangLi,LiuYu,Su BaoKu.An evolutionary particle filter based EM algorithm and its application[J].Journal of Harbin Institute Of Technology(New Series),2010,17(1):70-74.DOI:10.11916/j.issn.1005-9113.2010.01.013.
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An evolutionary particle filter based EM algorithm and its application
Author NameAffiliation
XiangLi Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China 
LiuYu Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China 
Su BaoKu Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China 
Abstract:
In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.
Key words:  particle filter  expectation-maximization (EM)  Gaussian mixture model (GMM)  nonlinear systems
DOI：10.11916/j.issn.1005-9113.2010.01.013
Clc Number:TN713
Fund:

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