Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks.
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.
Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Xuzhou 221116, China. gaoshouwan@cumt.edu.cn.
Shouwan
S
Gao
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. chenp@cumt.edu.cn.
Pengpeng
P
Chen
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. hdts15170041p2@cumt.edu.cn.
Dan
D
Huang
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. niuq@cumt.edu.cn.
Qiang
Q
Niu
Kalman filtering; Markov process; distributed sensing; packet losses; stability analysis