A Novel Adaptive Event-Triggered Approach for Distributed State Estimation in Wireless Sensor Networks

This paper addresses data transmission efficiency in peer-to-peer distributed sensor networks under communication-constrained environments, aiming to reduce communication bandwidth and extend network lifetime. To this end, a novel adaptive event-triggered Kalman filter is developed. Specifically, local information is only transmitted to neighbors when it significantly impacts the estimation results. An adaptive event-triggered threshold is designed based on the target's motion state, and the Kullback-Leibler(KL) Divergence used to describe the difference in target states. Finally, the effectiveness and superiority of the proposed algorithm is verified by simulation experiments.