An improved particle filtering algorithm based on observation inversion optimal sampling

作     者：胡振涛 潘泉 杨峰 程咏梅 

作者机构：College of AutomationNorthwestern Polytechnical University 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2009年第16卷第5期

页      面：815-820页

核心收录：

学科分类：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）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：Project(60634030) supported by the Key Project of the National Natural Science Foundation of China Project(60702066) supported by the National Natural Science Foundation of China Project (2007ZC53037) supported by Aviation Science Foundation of China Project(CASC0214) supported by the Space-Flight Innovation Foundation of China 

主　　题：particle filter proposal distribution re-sampling observation inversion 

摘      要：According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.