Space surveillance depends on efficiently directing sensor resources to maintain custody of known catalog objects. However, it remains unclear how to best utilize these resources to rapidly search for and track newly detected space objects. Provided a novel measurement, a search set can be instantiated through admissible region constraints to inform follow-up observations. In lacking well-constrained bounds, this set rapidly spreads in the along-track direction, growing much larger than a follow-up sensor's finite field of view. Moreover, the number of novel objects may be uncertain, and follow-up observations are most commonly corrupted by false positives from known catalog objects and missed detections. In this work, we address these challenges through the introduction of a joint sensor control and multi-target tracking approach. The search set associated to a novel measurement is represented by a Cardinalized Probability Hypothesis Density (CPHD), which jointly tracks the state uncertainty associated to a set of objects and a probability mass function for the true target number. In follow-up sensor scans, the information contained in an empty measurement set, and returns from both novel objects and known catalog objects is succinctly captured through this paradigm. To maximize the utility of a follow-up sensor, we introduce an information-driven sensor control approach for steering the instrument. Our methods are tested on two relevant test cases and we provide a comparative analysis with current naive tasking strategies.

翻译：空间监视依赖于高效调度传感器资源以维持对已知编目物体的持续跟踪。然而，如何最优利用这些资源来快速搜索并跟踪新探测到的空间物体，目前仍不明确。给定一个新测量值，可通过容许区域约束实例化一个搜索集，为后续观测提供依据。在缺乏良好约束边界的情况下，该搜索集会沿航迹方向迅速扩散，其规模远大于后续传感器的有限视场。此外，新物体的数量可能不确定，且后续观测最常受到来自已知编目物体的误报和漏检的干扰。在本工作中，我们通过引入一种联合传感器控制与多目标跟踪方法来解决这些挑战。与新测量值关联的搜索集由基数化概率假设密度（CPHD）表示，该方法能同时跟踪与一组物体相关的状态不确定性以及真实目标数量的概率质量函数。在后续传感器扫描中，空测量集所包含的信息，以及来自新物体和已知编目物体的回波，均可通过此范式简洁地捕获。为最大化后续传感器的效用，我们提出了一种信息驱动的传感器控制方法来引导仪器。我们的方法在两个相关测试案例上进行了验证，并与当前朴素的调度策略进行了对比分析。