Abstract The space object catalog provides orbital state and characteristic information of space objects for critical applications in space situational awareness. Maintaining accurate states for all objects in the catalog is essential, but it leads to large loads on sensors and limits the time available to search for new objects. This study proposes a novel sensor tasking method for search and catalog maintenance of space objects in Geosynchronous Earth Orbit (GEO). This new framework formulates sensor tasking as a multi-objective optimization problem. It seeks an optimal balance between sensor resources to search for new objects and to maintain precise state estimates for all objects in the catalog. In order to maintain custody of newly detected targets, an evidence-based decision-making method is used to effectively prompt follow-on tracking. The labeled multi-Bernoulli filter is employed to track existing and new space objects and provide refined orbital state estimation. Simulation results are presented, in which 100 cataloged GEO objects and 200 new GEO objects are tracked using a space-based sensor placed on a Sun-synchronous orbit.

中文翻译：

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用于地球同步空间物体搜索和目录维护的传感器任务

摘要 空间目标目录为空间态势感知的关键应用提供空间目标的轨道状态和特征信息。保持目录中所有对象的准确状态至关重要，但它会导致传感器负载过大，并限制了可用于搜索新对象的时间。这项研究提出了一种新的传感器任务方法，用于对地球同步轨道 (GEO) 中的空间物体进行搜索和编目维护。这个新框架将传感器任务制定为一个多目标优化问题。它寻求传感器资源之间的最佳平衡，以搜索新对象并维护目录中所有对象的精确状态估计。为了保持对新探测目标的监护，采用基于证据的决策方法，有效提示后续跟踪。标记的多伯努利滤波器用于跟踪现有和新的空间物体，并提供精细的轨道状态估计。展示了模拟结果，其中使用放置在太阳同步轨道上的天基传感器跟踪了 100 个编目 GEO 物体和 200 个新的 GEO 物体。