Event-based distributed state estimation for linear systems under unknown input and false data injection attack

• Published in: Signal processing Vol. 170; p. 107423
• Main Authors: Li, Li, Yang, Huan, Xia, Yuanqing, Yang, Hongjiu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: Distributed state estimation; Event-triggered transmission scheme; False data injection attack; Unknown input; False data injection attack; Distributed state estimation; Unknown input; Event-triggered transmission scheme
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.107423

•For each node, an event-triggered transmission scheme is designed to reduce the impact of attack on estimation performance.•A novel event-based distributed state estimator is derived, where filter gains are obtained by solving two Riccati-like difference equations.•Sufficient conditions are established to ensure the asymptotic boundedness of estimation error covariance. This paper investigates an event-based distributed state estimation problem for a linear system subject to unknown input and false data injection attack. The unknown input is treated as a process with a non-informative prior. An event-triggered transmission scheme is designed to reduce exchange of unreliable state estimate information between sensor nodes. In order to defend against false data injection attack, a novel event-based distributed state estimator is proposed by using state estimate information that comes from individual node and neighboring nodes, where filter gains are derived by minimizing an upper bound of estimation error covariance. Then sufficient conditions are established to ensure asymptotic boundedness of the designed estimation error covariance. Finally, effectiveness of the proposed technique is demonstrated by a numerical example.