Group Tracking of Space Objects within Bayesian Framework

It is imperative to efficiently track and catalogue the extensive dense group space objects for space surveillance. As the main instrument for Low Earth Orbit (LEO) space surveillance, ground-based radar system is usually limited by its resolving power while tracking the small space debris with high dense population. Thus, the obtained information about target detection and observation will be seriously missed, which makes the traditional tracking method inefficient. Therefore, we conceived the concept of group tracking. The overall motional tendency of the group objects is particularly focused, while the individual object is simultaneously tracked in effect. The tracking procedure is based on the Bayesian frame. According to the restriction among the group center and observations of multi-targets, the reconstruction of targets’ number and estimation of individual trajectory can be greatly improved on the accuracy and robustness in the case of high miss alarm. The Markov Chain Monte Carlo Particle (MCMC-Particle) algorism is utilized for solving the Bayesian integral problem. Finally, the simulation of the group space objects tracking is carried out to validate the efficiency of the proposed method.