Composite weighted average consensus filtering for space object tracking.

In this paper, a composite weighted average consensus filtering (CWACF) algorithm is proposed for space object tracking by combining two distributed heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimates from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it is proved that the estimation error is exponentially bounded in mean square. The performances of the proposed algorithm, the distributed extended Kalman filtering (DEKF) and the distributed sparse-grid quadrature filter (DSGQF) are compared in a space object tracking problem. • A composite weighted average consensus filtering (CWACF) algorithm is designed. • CWACF can provide precise estimation for space target tracking. • The estimation error of CWACF is proved to be exponentially bounded in mean square. • CWACF can achieve the balance between estimation accuracy and computational cost. [ABSTRACT FROM AUTHOR]