Distributed fusion estimation for multi-sensor non-uniform sampling systems with correlated noises and packet dropouts

In this paper, an optimal distributed fusion estimation algorithm is presented for multi-sensor non-uniform sampling systems with the correlated process noise and measurement noises at the same time and network-induced packet dropouts. First, the non-uniform sampling problem is first transformed to a single-rate synchronous sampling problem. Then, local optimal filters and the filtering error cross-covariance matrices between any two local filters are derived. Finally, the optimal distributed fusion filter is presented by using the matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed fusion approach.