Distributed filtering for discrete-time linear systems with fading measurements and time-correlated noise

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• 作者：Wenling Li^a ; ^b ; ^{lwlmath@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yingmin Jia^a ; ^b ; ^{ymjia@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Junping Du^c ; ^{junpingdu@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Distributed filtering ; Kalman filter ; Fading measurement ; Time-correlated measurement noise
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：211-219
• 全文大小：424 K

文摘

This paper studies the problem of distributed filtering for discrete-time linear systems with fading measurements and time-correlated noise over a sensor network. To address the problem of the time-correlated measurement noise, the measurement differencing approach is adopted to define a new measurement such that the noise in the new measurement is not time-correlated any longer. Based on the new measurement, the innovation-based and the consensus-based distributed filters are proposed for each sensor by using its neighboring information. By resorting to the graph properties, the filter gain matrices are designed for each sensor to develop optimal distributed filters in the sense of minimum variance. Moreover, suboptimal distributed filters are proposed to reduce the computational cost and the communication cost. The performance of the distributed filters is analyzed with respect to the fading factor. Simulation results are provided to show the effectiveness of the proposed filters.