An adaptive Kalman filter estimating process noise covariance

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• 作者：Hairong Wang^a ; ^bAuthor Vitae ; Zhihong Deng^a ; ^{dzh_deng@bit.edu.cn}Author Vitae ; Bo Feng^aAuthor Vitae ; Hongbin Ma^aAuthor Vitae ; Yuanqing Xia^aAuthor Vitae
• 关键词：Adaptive Kalman filter ; Unknown process noise covariance matrix ; Recursive covariance estimating ; Stability analysis
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 February 2017
• 年：2017
• 卷：223
• 期：Complete
• 页码：12-17
• 全文大小：920 K
• 卷排序：223

文摘

In this paper, a new adaptive Kalman filter algorithm is proposed to cope with the unknown a priori covariance matrix of process noise for the linear discrete-time systems. The process noise covariance matrix is estimated by the proposed algorithm based on the measurement sequence. Accordingly, we construct a new measurement sequence to sequentially estimate process covariance matrix in terms of the relationship between the measurement and process noise sequence. Then the stability of the proposed algorithm is analyzed. The algorithm shows a simple recursive form and great performance enhancement of application. Finally, the navigation simulation results are presented to illustrate the validity and practicality of the proposed algorithm.