Distributed Set-Membership Filtering for Sensor Networks: An Event-Triggered Approach

The distributed set-membership filtering is investigated for linear discrete systems with incomplete measurements. A new ellipsoid-based event-triggered mechanism is proposed, which utilizes the estimation ellipsoid to design the triggered condition and reduces the transmission frequency of the estimator state and the shape matrix. Then, a distributed filter based on the event-triggered mechanism and incomplete measurements is constructed, and a sufficient condition of the feasibility of the filter is derived using mathematical induction and set theory. The desired filter gains are obtained by addressing a series of constrained optimization problems. Furthermore, a fusion algorithm is introduced to reconstruct more accurate estimation information. The constraint of the measurement noise is then updated online based on the measurement and the reconstructed estimation information. Finally, the validity of the proposed method is verified by a simulation example.