Bayesian Group Tracking Method for Space Debris

With the intense increase in space debris, it is necessary to efficiently track and catalog the extensive dense clusters of space debris. As the main instrument for LEO space surveillance, a ground-based radar system is usually limited by its resolution while tracking small space debris with high density. Thus, the obtained measurement information could have been seriously missed, which makes the traditional tracking method inefficient. To address this issue, we conceived the concept of group tracking. For group tracking, the overall motional tendency of the group objects is expected to be revealed, and the trajectories of individual objects are simultaneously reconstructed explicitly. To model the interaction between the group center and individual trajectories using the MRF within a Bayesian framework, the objects' number and individual trajectory can be estimated more accurately. The MCMC-Particle algorithm was utilized for solving the Bayesian integral problem. Finally, simulation was carried out to validate the efficiency of the proposed method.