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Sensors 2016, 16(4), 566; doi:10.3390/s16040566

Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks

1
Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Xuzhou 221116, China
2
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 18 March 2016 / Revised: 13 April 2016 / Accepted: 17 April 2016 / Published: 20 April 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [981 KB, uploaded 20 April 2016]   |  

Abstract

This paper studies the remote Kalman filtering problem for a distributed system setting with multiple sensors that are located at different physical locations. Each sensor encapsulates its own measurement data into one single packet and transmits the packet to the remote filter via a lossy distinct channel. For each communication channel, a time-homogeneous Markov chain is used to model the normal operating condition of packet delivery and losses. Based on the Markov model, a necessary and sufficient condition is obtained, which can guarantee the stability of the mean estimation error covariance. Especially, the stability condition is explicitly expressed as a simple inequality whose parameters are the spectral radius of the system state matrix and transition probabilities of the Markov chains. In contrast to the existing related results, our method imposes less restrictive conditions on systems. Finally, the results are illustrated by simulation examples. View Full-Text
Keywords: Kalman filtering; packet losses; distributed sensing; stability analysis; Markov process Kalman filtering; packet losses; distributed sensing; stability analysis; Markov process
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Gao, S.; Chen, P.; Huang, D.; Niu, Q. Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks. Sensors 2016, 16, 566.

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