Distributed cubature Kalman filter based on observation bootstrap sampling  

作　　者：胡振涛 Hu Yumei Zheng Shanshan Li Xian Guo Zhen 

机构地区：[1]Institute of Image Processing and Pattern Recognition,Henan University

出　　处：《High Technology Letters》2016年第2期142-147,共6页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61300214);the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(13IRTSTHN021);the Post-doctoral Science Foundation of China(No.2014M551999);the Funding Scheme of Young Key Teacher of Henan Province Universities(No.2013GGJS-026)

摘　　要：Aiming at the adverse effect caused by observation noise on system state estimation precision,a novel distributed cubature Kalman filter(CKF) based on observation bootstrap sampling is proposed.Firstly,combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling,an observation bootstrap sampling strategy is designed.The objective is to deal with the adverse influence of observation uncertainty by increasing observations information.Secondly,the strategy is dynamically introduced into the cubature Kalman filter,and the distributed fusion framework of filtering realization is constructed.Better filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation system.Theory analysis and simulation results show the proposed algorithm feasibility and effectiveness.Aiming at the adverse effect caused by observation noise on system state estimation precision, a novel distributed cubature Kalman filter （CKF） based on observation bootstrap sampling is pro- posed. Firstly, combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling, an observation bootstrap sampling strategy is designed. The objective is to deal with the adverse influence of observation uncertainty by increasing observations information. Secondly, the strategy is dynamically introduced into the cuba- ture Kalman filter, and the distributed fusion framework of filtering realization is constructed. Better filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation system. Theory analysis and simulation results show the proposed algorithm feasi- bility and effectiveness.

关 键 词：state estimation cubature Kalman filter （CKF） observation bootstrap sampling distributed weighted fusion 

分 类 号：TN713[电子电信—电路与系统]