Commercial proposals for large constellations of satellites present a major problem in the field of Space Situational Awareness (SSA). Space traffic management systems must produce accurate and timely predictions of potential collisions for operators to perform evasive maneuvers. This requires precise orbit determination solutions, but limitations on sensor resources can impair tracker performance. This work presents an integrated tasking and tracking system that can autonomously task a network of ground sensors to track large constellations with the goal of identifying hazardous conjunctions. The sensor scheduling approach combines information gain maximization with predicted probability of collision to produce a single reward function for tasking. The labeled multi-Bernoulli filter, a multi-target filter capable of resolving data ambiguity, processes the data and the resulting catalog estimate drives another round of scheduling. A tracking simulation involving 4,545 satellites in low-Earth orbit and a network of twelve ground-based sensors demonstrates how this scheduling method can focus on high-risk targets while maintaining custody of the entire constellation. A conjunction assessment is performed using the estimated catalog at the end of the tracking phase, and the accuracy of the analysis is used to evaluate the scheduling method’s efficacy.

大型卫星星座的商业提案是空间态势感知 (SSA) 领域的一个主要问题。空间交通管理系统必须准确及时地预测潜在碰撞，以便操作员进行规避操作。这需要精确的轨道确定解决方案，但传感器资源的限制会影响跟踪器的性能。这项工作提出了一个集成的任务和跟踪系统，该系统可以自主地为地面传感器网络分配任务以跟踪大型星座，以识别危险的连接点。传感器调度方法将信息增益最大化与预测的碰撞概率相结合，以产生用于任务的单一奖励函数。标记的多伯努利滤波器，一种能够解决数据歧义的多目标滤波器，处理数据和由此产生的目录估计驱动另一轮调度。一项涉及 4,545 颗低地球轨道卫星和 12 个地面传感器网络的跟踪模拟演示了这种调度方法如何能够专注于高风险目标，同时保持对整个星座的监管。在跟踪阶段结束时使用估计的目录进行联合评估，分析的准确性用于评估调度方法的有效性。