Cost Reference Particle Filter Based on Adaptive Particle Swarm Optimization in Observation Uncertainty

被引：0
|
作者：
Hu Zhen-Tao [1 ]
Liu Xian-Xing [1 ]
Jin Yong [1 ]
机构：
[1] Henan Univ, Lab Image Proc & Pattern Recognit, Kaifeng 475001, Henan, Peoples R China
来源：
2011 30TH CHINESE CONTROL CONFERENCE (CCC) | 2011年
关键词：
Nonlinear Filter; Cost Reference Particle Filter; Particle Swarm Optimization; Observation Uncertainty; ALGORITHM;
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：
Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty, a novel cost reference particle filter based on adaptive particle swarm optimization is proposed. In the new algorithm, the cost function and the risk function are firstly introduced to realize reasonable utilization of the latest observation. In addition, according to the prior modeling information, a new adaptive method is given to solve the selection of limit velocity. And then the movement of particle set towards the region of high weight particle is completed by particle swarm optimization strategy. The algorithm realizes the dynamic combination of the cost reference particle filter and the adaptive particle swarm optimization, and the reliability and stabilize of sampling particle set relative to system state are improved. The theoretical analysis and experimental results show the efficiency of the proposed algorithm.
引用
收藏
页码：796 / 800
页数：5
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