Distributed Diffusion Cubature Kalman Filtering Based on Data Compression and Fast Covariance Intersection
Jingang Liu, Yuhang Yang, Zeqi Zhang, Shenmin Song · IEEE Sensors Journal · 2024
In this article, a diffusion cubature Kalman filtering based on data compression and fast covariance intersection (CDCKF-FCI) is proposed for nonlinear sensor networks. The weighted measurement fusion algorithm is applied to compress the measurements of the sensor and its neighbor nodes to obtain a compressed measurement. Based on the compressed measurement and cubature Kalman filter (CKF), a local estimate is derived for each sensor. It does not involve the computation and exchange of pseudo measurement matrices, avoiding the loss of accuracy with statistical linearization and the communication burden between nodes. Subsequently, this approach is proved to be numerically equivalent to centralized fusion. Considering that the correlation of nodes is unknown or unavailable, a fast covariance intersection (CI) fusion algorithm is used for diffusion fusion. It fully considers the estimation errors of the nodes at different time and does not require complex optimization process. Finally, it is proved that the mean performance and the estimation error are exponentially bounded in mean square under some assumptions. A simulation example shows the effectiveness of the proposed algorithm.