Distributed sigma-point Kalman filtering for sensor networks: Dynamic consensus approach

A scalable Sigma-Point Kalman filter (DSPKF) is proposed for distributed target tracking in a sensor network in this paper. The main idea is to use dynamic consensus strategy to the information form sigma-point Kalman filter (ISPKF) that derived from weighted statistical linearization perspective. Each node estimates the global average information contribution by using local and neighbors´ information rather than by the information from all nodes in the network. Therefore, the proposed DSPKF algorithm is completely distributed and applicable to large-scale sensor network. A novel dynamic consensus filter is proposed, and its asymptotical convergence performance and stability are discussed. Finally, a numerical example is given to illustrate the proposed scheme.