Distributed resilient filtering for time-varying systems over sensor networks subject to Round-Robin/stochastic protocol

• Published in: ISA transactions Vol. 87; pp. 55 - 67
• Main Authors: Sheng, Li, Niu, Yichun, Gao, Ming
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2019
• Subjects: Distributed [formula omitted] filtering; Nonlinear time-varying systems; Recursive linear matrix inequality; Resilient filter; Round-Robin protocol; Stochastic protocol; Nonlinear time-varying systems; Round-Robin protocol; Resilient filter; Stochastic protocol; Distributed H∞ filtering; Recursive linear matrix inequality; Distributed filtering
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2018.11.012

This paper is concerned with the distributed resilient filtering for nonlinear time-varying systems over sensor networks with communication protocol constraints. In order to prevent data collisions over sensor networks, the data transmission between each node and its neighboring ones is realized through the Round-Robin/stochastic protocol, which guarantee that only one neighboring node is permitted to send message at each transmission instant. Moreover, the resilient filter is designed to suppress the adverse effects induced by imprecision of filter parameters. By means of the stochastic analysis approach and recursive linear matrix inequality technique, several sufficient conditions are established to ensure that the state estimation error system subject to communication protocols satisfies the prescribed H∞ performance constraint over a finite-horizon. Finally, a numerical example is presented to show the feasibility and effectiveness of the proposed results. •Protocols are employed in sensor networks to prevent data collisions.•The distributed filter is designed for time-varying systems over sensor networks.•The resilient filter is applied to reduce the adverse effects caused by imprecision.•A recursive algorithm is proposed to derive parameters of distributed filter.