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

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.