An evolutionary particle filter based EM algorithm and its application
XiangLi, LiuYu, 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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An evolutionary particle filter based EM algorithm and its application
本站小编 哈尔滨工业大学/2019-10-23
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