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

Abstract

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.