Moving-Horizon Estimation for Linear Dynamic Networks With Binary Encoding Schemes

• 成果类型: Article
• 署名作者: Liu, Qinyuan; Wang, Zidong
• 署名单位: Tongji University; Tongji University; Shandong University of Science & Technology; Brunel University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2996579
• 发表日期: 2021
• 页码: 1763-1770
• 关键词: Encoding; State estimation; Kalman filters; Robustness; distortion; Finite wordlength effects; Binary encoding schemes; communication constraints; dynamic networks; Kalman filtering; moving-horizon estimation; remote state estimation

摘要

This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks. The signals are transmitted via noisy network channels and distortions can be caused by channel noises. As such, the binary encoding schemes, which take advantages of the robustness of the binary data, are exploited during the signal transmission. More specifically, under such schemes, the original signals are encoded into a bit string, transmitted via memoryless binary symmetric channels with certain crossover probabilities, and eventually restored by a decoder at the receiver. Novel centralized and decentralized moving-horizon estimators in the presence of the binary encoding schemes are constructed by solving the respective global and local least-square optimization problems. Sufficient conditions are obtained through intensive stochastic analysis to guarantee the stochastically ultimate boundedness of the estimation errors. A simulation example is presented to verify the effectiveness of the proposed moving-horizon estimators.