Improved particle filter based on ensemble Kalman filter

被引：1

作者：

Du H.-Y. ^{[1
]}

Hao Y.-L. ^{[1
]}

Zhao Y.-X. ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2011年 / 33卷 / 07期

关键词：

Ensemble Kalman filter (EnKF); Nonlinear system; Particle filter; Proposal distribution function;

D O I：

10.3969/j.issn.1001-506X.2011.07.42

中图分类号：

学科分类号：

摘要：

An improved particle filter based on the ensemble Kalman filter (EnKF) is described. The EnKF is used to propagate the particle's proposal distribution of particle filter in every time step. Because the EnKF can attain a maximum posterior estimation of the nonlinear system, and the proposal distribution function integrates the latest observation into system state transition density, so the proposal distribution can approximate the true posterior distribution more accurately. This new algorithm uses sample ensembles to approximate the nonliner proposal distribution, and the number of ensembles required in the EnKF is flexible. In this way, the accuracy and efficiency of the algorithm is improved. The simulation results reflect that the novel particle filter is superior to the standard particle filter, the extended Kalman particle filter and the unscented particle filter.

引用

页码：1653 / 1657

页数：4

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