Stochastic Event-Triggered Variational Bayesian Filtering

• 成果类型: Article
• 署名作者: Lv, Xiaoxu; Duan, Peihu; Duan, Zhisheng; Chen, Guanrong; Shi, Ling
• 署名单位: Peking University; City University of Hong Kong; Hong Kong University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3203015
• 发表日期: 2023
• 页码: 4321-4328
• 关键词: Index Terms-Event-based scheduling; Kalman filter; remote state estimation; variational Bayesian

摘要

This article proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.