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.

本文提出了一种分布式扩散无迹卡尔曼滤波算法提出了基于协方差交叉策略（DDUKF-CI）的间歇测量目标跟踪。借助伪测量矩阵，将具有间歇观测的标准无迹卡尔曼滤波（UKF）转化为扩散算法的信息形式，以融合来自邻居的中间信息，提高估计性能。考虑到传感器网络中的未知相关性，协方差交叉（CI）策略与扩散算法相结合。此外，证明了所提出的 DDUKF-CI 是一致的，并且使用随机稳定性理论估计误差在均方中呈指数有界。最后，