Tracking of Group Space Objects within Bayesian Framework

Abstract

It is imperative to efficiently track and catalogue the extensive dense group of space objects for space surveillance. As the main instrument for Low Earth Orbit (LEO) space surveillance, ground-based radar systems are usually limited by their resolving power while tracking small, but very dense clusters of space debris. Thus, the information obtained regarding target detection and observation will be seriously compromised, making the traditional tracking method inefficient. Therefore, we conceived the concept of group tracking. The overall motional tendency of a group’s objects is particularly focused, while individual objects are in effect simultaneously tracked. The tracking procedure is based on the Bayesian framework. According to the restriction among the group center and observations of multi-targets, the reconstruction of the number of targets and estimation of individual trajectories can be greatly improved with respect to the accuracy and robustness in the case of high miss alarm. The Markov Chain Monte Carlo Particle (MCMC-Particle) algorithm is utilized to solve the Bayesian integral problem. Finally, the simulation of the tracking of group space objects is carried out to validate the efficiency of the proposed method.