Event-Triggered Distributed Moving Horizon Estimation for Smart Sensor Networks With Fading Channels and Constraints

Abstract

This paper addresses the distributed state estimation problem of smart sensor networks affected by fading channels and constraints. An event-triggered distributed moving horizon estimation method is proposed from a robust perspective. Specifically, by considering the worst-case scenario of fading channels, the estimator in our proposed method is formulated as a minimax optimization problem with bound constraints. An event trigger mechanism (ETM) is incorporated to regulate information exchange among nodes. Sufficient conditions ensuring the local input-to-state consistent stability (LISCS) and the input-to-state stability (ISS) of estimation errors are established. To satisfy these conditions, the estimator’s parameters are derived by solving linear matrix inequalities (LMIs). Additionally, two approximate solutions for the estimator are presented to reduce the computational burden of online estimation. Finally, the simulation comparison and experimental results demonstrate the effectiveness and practicability of our proposed method.