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Distributed cubature Kalman filter based on observation bootstrap sampling
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作者 胡振涛 Hu Yumei +2 位作者 Zheng Shanshan Li Xian Guo Zhen 《High Technology Letters》 EI CAS 2016年第2期142-147,共6页
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 ... 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. 展开更多
关键词 state estimation cubature Kalman filter (CKF) observation bootstrap sampling distributed weighted fusion
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