A Dynamic Event-Triggered Method to Distributed Filtering with Switching Nonlinearities and Redundant Channels

Abstract

This paper is concerned with the dynamic event-triggered distributed filtering problem for a class of time-varying stochastic systems with switching nonlinearities and redundant channels. A random variable that obeys Bernoulli distribution is used to characterize the switching nonlinearities, and moreover, a set of random variables are used to described redundant channels, which are introduced to increase the probability of successful deliver of the data packets. A dynamic event-triggered scheme is introduced to further reduce the number of excessive executions of the signal transmissions. The aim of this paper is to design a locally optimal time-varying filter such that, for switching nonlinearities and redundant channels, an upper bound on the filtering error covariance is derived and such an upper bound is minimized by properly designing the filter gain based on the solution of a Riccati-like difference equation. In addition, the performance analysis of the proposed filtering algorithm is conducted and a sufficient condition is given to verify the boundedness of the filtering error. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.