Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

作     者：胡振涛 Liu Xianxing Li Jie 

作者机构：Institute of Image Processing and Pattern RecognitionHenan University 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2014年第20卷第1期

页      面：34-41页

核心收录：

学科分类：08[工学] 0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 0807[工学-动力工程及工程热物理] 080203[工学-机械设计及理论] 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] 0836[工学-生物工程] 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）] 0702[理学-物理学] 

基　　金：Supported by the National Natural Science Foundations of China(No.61300214,61170243) the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021) the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066) the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148) the Funding Scheme of Young Key Teacher of Henan Province Universities,and the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07) 

主　　题：multi-sensor information fusion particle filter weight optimization predictionand update 

摘      要：The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.