Distributed Self-Tuning Filters for Networked Stochastic Uncertain Systems with Unknown Noise Variances of Stochastic Deception Attacks

This paper studies the distributed self-tuning security estimation problems for networked stochastic uncertain systems with correlated process and measurement noises and unknown noise variances of deception attack signals. A correlation function method is adopted to identify the unknown noise variances of the stochastic deception attack signals. Further, a distributed self-tuning filter is obtained based on compressed measurements by a weighted measurement fusion method. Its convergence is analyzed. A Simulation example verifies the effectiveness of the proposed algorithm.