Cost Reference Particle Filter Based on Adaptive Particle Swarm Optimization in Observation Uncertainty
会议名称：《第三十届中国控制会议》
会议日期：2011年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(No60972119) the Henan province Natural Science Foundation(No2008A510001) the Henan University Natural Science Foundation(No2010YBZR045)
关 键 词：Nonlinear Filter Cost Reference Particle Filter Particle Swarm Optimization Observation Uncertainty
摘 要：正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 *** the new algorithm,the cost function and the risk function are firstly introduced to realize reasonable utilization of the latest *** addition, according to the prior modeling information,a new adaptive method is given to solve the selection of limit *** then the movement of particle set towards the region of high weight particle is completed by particle swarm optimization *** 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 *** theoretical analysis and experimental results show the efficiency of the proposed algorithm.