Design of Event-Based Resilient Distributed Filtering Algorithm for Time-Varying Stochastic Systems with Correlated Noises over Sensor Networks

Abstract

An event-triggered (ET) recursive distributed filtering approach is designed for a class of stochastic systems with correlated noises. The correlated noises are represented by known matrices and the Kronecker $\delta$ function. The ET mechanism that can regulate the sensor information is employed. In addition, the perturbation of the filter gain is considered to suppress the effects of the gain variation on filtering accuracies. An upper bound with respect to the filtering error covariance that can be minimized is obtained by properly choosing the filter gain. Finally, an illustrative example is given to verify the usefulness of the proposed filtering approach.