In order for close proximity satellites to safely perform their missions, the relative states of all satellites and pieces of debris must be well understood. This presents a problem for ground based tracking and orbit determination since it may not be practical to achieve the required accuracy. Using space-based sensors allows for more accurate relative state estimates, especially if multiple satellites are allowed to communicate. Of interest to this work is the case where several communicating satellites each need to maintain a local catalog of communicating and non-communicating objects using angles-only limited field of view (FOV) measurements. However, this introduces the problem of efficiently scheduling and coordinating observations among the agents. This paper presents a decentralized task allocation algorithm to address this problem and quantifies its performance in terms of fuel usage and overall catalog uncertainty via numerical simulation. It was found that the new method significantly outperforms the uncertainty-fuel Pareto frontier formed by current approaches.

翻译：为使近距离卫星能够安全执行任务，必须充分掌握所有卫星及碎片目标的相对状态。地面跟踪与定轨系统难以达到所需精度，这构成了实际挑战。利用天基传感器可获取更精确的相对状态估计，尤其在多颗卫星具备通信能力的情况下。本研究关注以下场景：多颗具备通信能力的卫星需通过纯角度有限视场测量，各自维护包含通信目标与非通信目标的局部编目。然而，这引发了智能体间观测任务的高效调度与协调问题。本文提出一种去中心化任务分配算法以解决该问题，并通过数值仿真从燃料消耗和整体编目不确定度两个维度量化算法性能。研究发现，新方法在不确定度-燃料帕累托前沿上显著优于现有方法。