Distributed Malicious Maneuvering Target Tracking Algorithm With Finite-Time Fusion Analysis

This paper proposes a distributed tracking framework for high-maneuvering and malicious maneuvering target based on the sensor network. Considering some of sensors in the network can only observe partial system output, the collective observability is guaranteed. Based on the collective observability, the distributed target tracking algorithm is designed by local filter phase and consensus fusion phase. In local filter phase, the local estimate results can be obtained by each sensor node. Based on the target kinematic and sensor observation equations, the Kalman-like estimation algorithm is proposed to obtain the local estimate results. In most of the exist distributed state estimation (DSE) algorithms, the excellent target tracking performance is based on the assumption that the fusion times is infinity in consensus fusion phase. In practical application, however, the consensus fusion phase should be completed during two sampling instants. Besides, based on the finite-time stability theory, the error covariance matrix is processed as the solution of discrete-time algebraic Riccati equation (DARE). Then the stability of the proposed algorithm with finite-time consensus fusion is analyzed. Finally, a numerical example for tracking maneuvering target is given to validate the effectiveness.