Distributed Extended Kalman-consensus Filtering Algorithm Based on Sensor Network for Nonlinear System
Zheng Zhang, Xiwang Dong, Qingke Tan, Yuan Liang, Qingdong Li, Zhang Ren · 2018
Distributed state estimation (DSE) in collaborative tracking realm has received considerable attention in recent years. Conventional centralized filtering algorithms have encountered a series of problems, such as the poor robustness and the high communication overhead. To deal with these problems, this paper studies the distributed state estimation algorithm over mobile sensor networks. The focus is on developing a consensus-based distributed filtering algorithm, which named extended Kalman-consensus filter (EKCF) algorithm. The EKCF algorithm is consist of two stages. During the first stage, each sensor uses local information to obtain the state estimation based on extended Kalman filtering algorithm. In the second stage, each sensor communicates with its neighbors by performing the consensus algorithm. The sufficient conditions and the detailed proof for the boundedness of the EKCF is proposed. Simulation results of the formation tracking model verifies the effectiveness of EKCF algorithm is proposed in this paper.