Expectation-Maximization-Based Remote State Estimation Under State-Dependent Packet Dropouts: From Maximum A Posteriori Perspective

• 成果类型: Article
• 署名作者: Nie, Yao; Wang, Zidong; Liu, Qinyuan
• 署名单位: Tongji University; Tongji University; Tongji University; Brunel University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3466873
• 发表日期: 2025
• 页码: 1608-1622
• 关键词: estimation; State estimation; CONVERGENCE; stability analysis; Kalman filters; Heuristic algorithms; Covariance matrices; expectation-maximization (EM) algorithm; Kalman filter (KF); maximum a posterior; remote estimation; state-dependent packet dropouts (SDPDs)

摘要

This article is concerned with the problem of remote state estimation for linear discrete-time systems with packet dropouts. The packet dropouts are state dependent, which occur only when the real-time target state enters specific observation-denied regions. At the remote estimator, the batch and iterative expectation-maximization (EM) algorithms are proposed for computing the optimal estimation in the maximum a posteriori (MAP) sense. It is shown that the iterative estimator exhibits a structure similar to that of an information-like type filter. The convergence of the EM algorithm for MAP estimation is investigated, and it is proven that the sequence of estimates converges to a local optimal solution. The computational complexities of both the batch and iterative algorithms are analyzed. Furthermore, a posterior Cram & eacute;r-Rao lower bound is derived to provide an offline performance bound for the proposed estimator. Finally, the effectiveness of the proposed estimator is demonstrated through an illustrative example.