Event‐triggered Kalman consensus filter over sensor networks

Abstract

Kalman consensus filter (KCF) has been developed for distributed state estimation over sensor networks where local estimates are exchanged with time‐triggered transmission mechanism. To reduce the amount of data transfer in sensor networks, the authors propose a KCF with an event‐triggered communication protocol. The triggering decision is based on the send‐on‐delta data transmission mechanism: each sensor transmits its local estimates to its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. On the basis of the event‐triggered communication protocol, an optimal Kalman gain matrix is derived by minimising the mean squared errors for each sensor and a suboptimal KCF is developed for scalable considerations. By using the Lyapunov‐based approach, a sufficient condition is presented for ensuring the stochastic stability of the suboptimal KCF. A numerical example is provided to verify the effectiveness of the proposed filter.