Resilient Distributed Filtering for Discrete Time-varying Systems with Missing Measurements and Stochastic Uncertainties

• Published in: International journal of control, automation, and systems Vol. 19; no. 5; pp. 1807 - 1818
• Main Authors: Liu, Tianqi, Chen, Dongyan, Hu, Jun
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.05.2021; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Control; Control Theory and Applications; Discrete time systems; Engineering; Filtration; Mathematical models; Mechatronics; Random variables; Robotics; Sensors; Topology; Uncertainty; Upper bounds; Wireless networks; Wireless sensor networks; 제어계측공학; variance-constrained method; resilient distributed filtering; Missing measurements; stochastic uncertainties; sensor networks
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-020-0051-3

In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain time-varying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the missing measurements phenomenon of each sensor node. In addition, the stochastic uncertainties are characterized by the multiplicative noises and stochastic nonlinearities. By using the variance-constrained method, an appropriate filter gain is selected to minimize the trace of the upper bound for the filter error covariance. Moreover, the resilient distributed filtering algorithm is designed and a new matrix simplification technique is introduced to deal with the sparsity of sensor networks topology. Finally, both the feasibility and effectiveness of the resilient distributed filtering algorithm are verified by a numerical simulation.