Variance-Constrained Distributed Filtering for Time-Varying Systems With Multiplicative Noises and Deception Attacks Over Sensor Networks

This paper is concerned with the variance-constrained distributed filtering problem for a class of time-varying systems subject to multiplicative noises, unknown but bounded disturbances and deception attacks over sensor networks. The available measurements at each sensing node are collected not only from the individual sensor but also from its neighbors according to the given topology. A new deception attack model is proposed where the malicious signals are injected by the adversary into both control and measurement data during the process of information transmission via the communication network. By resorting to the recursive linear matrix inequality approach, a sufficient condition is established for the existence of the desired filter satisfying the prespecified requirements on the estimation error variance. Subsequently, an optimization problem is formulated in order to seek the filter parameters ensuring the locally optimal filtering performance at each time instant. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.