The iterated extended kalman particle filter

被引：0
|
作者：
Li, LQ [1 ]
Ji, HB [1 ]
Luo, JH [1 ]
机构：
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
来源：
International Symposium on Communications and Information Technologies 2005, Vols 1 and 2, Proceedings | 2005年
关键词：
particle filtering; iterated extended kalman filter; the unscented particle filter;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Particle filtering shows great promise in addressing a wide variety of non-linear and/or non-Gaussian problem. A crucial issue in particle filtering is the selection of the importance proposal distribution. In this paper, the iterated extended kalman filter (IEKF) is used to generate the proposal distribution. The proposal distribution integrates the latest measurements into system state transition density, so it can match the posteriori density well. The simulation results show that the new particle filter superiors to the standard particle filter and the other filters such as the unscented particle filter (UPF), the extended kalman particle filter (PF-EKF), the EKF.
引用
收藏
页码：1172 / 1175
页数：4
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