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《Acta Electronica Sinica》 2017-04
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Multi-Sensor Ensemble Kalman Filtering Algorithm Based on Metropolis-Hastings Sampling

HU Zhen-tao;ZHANG Jin;HU Yu-mei;JIN Yong;Institute of Image Processing and Pattern Recognition,Henan University;College of Automation,Northwestern Polytechnical University;  
Recently, ensemble Kalman filter is considered as an effective solution for the state estimation of nonlinear system. Aiming at the consistency deviation occurred in virtual measurement sampling process on account of measurement noise uncertainty,a novel multi-sensor ensemble Kalman filtering algorithm based on Metropolis-Hastings sampling is proposed.Firstly, combined with the physical properties of multi-sensor measurement system and the generation mechanism of bootstrapping measurement in ensemble Kalman filter,multi-sensor bootstrapping measurement set is structured. Secondly,through solving the likelihood of multi-sensor bootstrapping measurement and designing the probability function of measurement acceptance,validation measurement from multi-sensor bootstrapping measurement set is confirmed by Metropolis-Hastings sampling strategy. The new method corrects the consistency deviation appearing at bootstrapping measurement by means of the extraction and utilization for the redundancy and complementary information in multi-sensor measurement, and improves the filtering precision for the estimated system state. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of our proposed algorithm.
【Fund】: 国家自然科学基金(No.61300214);; 河南省高校科技创新团队支持计划(No.13IRTSTHN021);; 中国博士后科学基金(No.2014M551999);; 河南省高校青年骨干教师资助计划(No.2013GGJS-026)
【CateGory Index】: TP212;TN713
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