Recursive distributed filtering over sensor networks on Gilbert–Elliott channels: A dynamic event-triggered approach

Abstract

In this paper, the recursive distributed filtering problem is investigated for a class of discrete nonlinear time-varying systems under a dynamic event-triggered mechanism. The system outputs are collected through a sensor network subject to a time-varying topology that is connected via Gilbert–Elliott channels and governed by a set of Markov chains. In order to save communication cost, a dynamic event-triggered strategy is utilized to decide when a particular sensor node should broadcast the corresponding measurement output to its neighbors. For the addressed time-varying systems, our aim is to design a distributed filter for each sensor node such that an upper bound on the filtering error variance is guaranteed and subsequently minimized at each iteration under the dynamic event-triggered transmission protocol. The parameters of the desired filter are obtained recursively by following a certain set of recursions. Finally, an illustrative example is employed to verify the effectiveness of the proposed dynamic event-triggered distributed filtering algorithm.