Distributed fusion filter for multi-sensor systems with random sensor delays, multiple packet dropouts and uncertain observations

This paper is concerned with the distributed fusion filtering problem for linear discrete-time stochastic systems measured by multiple sensors with random sensor delays, packet dropouts and uncertain observations. By state augmentation, the original system with mixed uncertainties of sensor delays, packet dropouts and uncertain observations is converted to a stochastic parameterized system. Based on this stochastic parameterized system, the local optimal linear filter based on single sensor is presented via an innovation analysis approach. Filtering error cross-covariance matrix between any two local filters is derived. Further, the distributed fusion filter is obtained based on the matrix-weighted optimal fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithm.