Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
Xingyu Zhou, Shuo Wang, Tong Qin
Abstract
This paper proposed a novel multi-spacecraft tracking and data association method based on the orbit uncertainty propagation. The proposed method makes full use of the dynamic information and thus the data association performance is enhanced. The proposed method is divided into three portions, i.e., the uncertainty propagation, the data association, and the orbit estimation. The second-order solutions derived for state and measurement prediction, on which to base the optimal association, are set up. The optimal association is solved by the contract network algorithm to reduce the computing cost. Finally, a second-order extended Kalman filter is designed to estimate the orbit of each spacecraft. The proposed method is successfully applied for solving a four-spacecraft tracking problem. Simulations show that all the four targets are well tracked. The method demonstrates close to 100% data association precision. The proposed method is proved to be efficient and effective to solve the multi-spacecraft tracking problem.