Fusion Filtering for Stochastic Systems with Correlated Noises and Deception Attacks

This paper is concerned with fusion filtering problem for a class of stochastic systems subject to both correlated noises and deception attacks. The system noise and measurement noises of different sensors are cross-correlated at the same time instant. During the data transmission over network, the deception attack signals are injected randomly, whose statues are described by a group of Bernoulli variables. The centralized fusion filter is proposed via an innovation analysis approach. In order to improve robustness and reduce computational burden, a distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. A simulation example is given to show the effectiveness of the proposed algorithms.