Distributed fusion filter for stochastic uncertain system with time-correlated noises and packet dropout compensations

In this paper, the distributed fusion filtering problem for stochastic uncertain systems with packet-dropping compensation and time-correlated noises is studied. The time-correlated process and measurement noises are described by the first-order Gaussian-Markov process. The uncertainties of model parameters are described by correlated white multiplicative noises. When the sensor measurement signal is lost over network transmission, its one-step prediction is used for compensation. Based on the local filters proposed in the recent literature, the filtering error cross-covariance matrices between any two local filters are derived. Further, a distributed fusion filter weighted by matrices is presented based on the distributed weighted fusion algorithm in the linear minimum variance (LMV) sense. The simulation results verify the effectiveness of the proposed algorithm.