Distributed fusion filter for multi-sensor systems with multiple random measurement delays and packet dropouts

This paper studies the distributed optimal fusion estimation problem for multi-sensors systems with multiple measurement delays and packet dropouts. Firstly, we define a new augmented state vector with a lower dimension. Based on the defined state, the local filter (LF) is given. Then, by applying the weighted fusion algorithms in the linear minimum variance sense, three distributed fusion filters weighted by matrix (FFWM), by diagonal matrix (FFWDM) and by scalars (FFWS) are obtained, respectively. The cross-covariance matrices between any two local filtering errors are derived to compute the fusion weights. Finally, simulation research verifies the effectiveness of the proposed distributed fusion filters.