基于贝叶斯框架的空间群目标跟踪技术

Tracking of Group Space Objects within Bayesian Framework

对大量密集的空间群目标进行有效跟踪、编目已成为空间监测的迫切需求。地基雷达作为近地轨道空间监测的主要手段,在对高密度的空间小碎片云进行跟踪时,通常会由于分辨能力有限,造成对个体目标检测、观测信息严重缺失,使得传统的多目标跟踪技术难以奏效。为此,该文基于"群"跟踪的概念,在贝叶斯框架下以群目标的整体运动趋势为跟踪对象,同时兼顾个体的运动目标轨迹跟踪,通过建立群目标的中心和观测量之间的相互作用约束模型,可以提升在漏警概率较高情况下的目标数目估计的稳健性以及单个目标的跟踪精度。贝叶斯积分的求解过程通过MCMC-Particle算法具体实现。通过对空间群目标跟踪的仿真实验验证了群跟踪技术的有效性。

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 trucking 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.