Remote State Estimation With Smart Sensors Over Markov Fading Channels

• 成果类型: Article
• 署名作者: Liu, Wanchun; Quevedo, E. Daniel; Li, Yonghui; Johansson, Karl Henrik; Vucetic, Branka
• 署名单位: University of Sydney; Queensland University of Technology (QUT); Royal Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3090741
• 发表日期: 2022
• 页码: 2743-2757
• 关键词: Intelligent sensors; Markov processes; Channel estimation; wireless sensor networks; Wireless communication; Stability criteria; Numerical stability; estimation; Kalman filtering; Linear systems; Markov fading channel; mean-square error (MSE); STABILITY

摘要

We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated multistate Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance in terms of the state transition matrix of the LTI system, the packet drop probabilities in different channel states, and the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach and provide new elementwise bounds of matrix powers. The stability condition is verified by numerical results and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or nonconvex depending on the transition probability matrix of the Markov channel states. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator) though the smart sensor setup achieves a better estimation performance.