Remote State Estimation With Posterior-Based Stochastic Event-Triggered Schedule

• 成果类型: Article
• 署名作者: Hu, Zhongyao; Chen, Bo; Wang, Rusheng; Yu, Li
• 署名单位: Zhejiang University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3278943
• 发表日期: 2024
• 页码: 1194-1201
• 关键词: State estimation; schedules; Probability density function; Technological innovation; Random variables; Bayes methods; wireless sensor networks; Bayesian filter; Kalman filter (KF); stability analysis; State estimation; stochastic event-triggered (SET) schedule

摘要

In this article, the authors aim to study the state estimation problem under the stochastic event-triggered (SET) schedule. A posterior-based SET mechanism is proposed, which determines whether to transmit data by the effect of the measurement on the posterior estimate. Since this SET mechanism considers the whole posterior probability density function, it has better information screening capability and utilization than the existing SET mechanisms that only consider the first-order moment information of measurement and prior estimate. Then, based on the proposed SET mechanism, the corresponding exact minimum mean square error estimator is derived by Bayes rule. Moreover, the prediction error covariance of the estimator is proved to be bounded under moderate conditions. Meanwhile, the upper and lower bounds on the average communication rate are also analyzed. Finally, two different systems are employed to show the effectiveness and advantages of the proposed methods.