Distributed diffusion unscented Kalman filtering based on covariance intersection with intermittent measurements

In this paper, a distributed diffusion unscented Kalman filtering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for target tracking with intermittent measurements. By virtue of the pseudo measurement matrix, the standard unscented Kalman filtering (UKF) with intermittent observations is transformed to the information form for the diffusion algorithm to fuse intermediate information from neighbors and improve the estimation performance. Considering unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the proposed DDUKF-CI is consistent and the estimation error is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman filtering (CUKF) are compared in a target tracking problem with a sensor network.