发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对近地轨道交会不确定性，提出机会约束信念空间蒙特卡洛树搜索规划器，权衡等待跟踪信息与机动干预，在NASA数据集上验证，表明跟踪质量与频率决定干预时机。
AI 中文摘要
近地轨道中交会事件频率的不断增加，给航天器操作员带来了越来越大的压力，他们不仅需要确定某次交会是否需要采取缓解措施，还需要确定是否有足够的信息来承诺执行机动。这项工作将这种信息-行动权衡表述为一个信念空间规划问题，针对一个可机动航天器与一个不可机动次级目标之间的交会场景。规划器将不确定的轨道状态表示为高斯信念，并使用一种机会约束的信念空间蒙特卡洛树搜索框架，在最近接近时刻（TCA）之前对可能的未来跟踪更新进行推理。一个终端机会约束限制了在预设碰撞风险阈值以上到达TCA的概率，使得规划器能够在等待信息丰富的跟踪的同时，在延迟变得过于危险时进行干预。我们在NASA交会评估风险分析数据集中的八个历史交会事件上评估了该方法。通过改变次级目标的测量质量和跟踪频率，我们生成了总共96个不同的评估场景。在所有评估条件下，规划器在约40%的回合中无需机动即可到达TCA，同时保持无终端碰撞风险违规。相比之下，固定时间的基于规则的机动策略在干预被推迟到更接近TCA时，能解决更多无需机动的交会，但代价是增加了终端风险违规。无需机动即可到达TCA的回合比例强烈依赖于跟踪质量和测量频率，范围从在准确、频繁测量下的76%到在最差跟踪条件下的约18%-20%。这些结果表明，跟踪质量和频率不仅是碰撞风险估计的输入：它们可以决定何时必须进行干预。
英文摘要
Increasing conjunction frequency in low Earth orbit places growing pressure on spacecraft operators to determine not only whether an encounter requires mitigation, but whether sufficient information is available to commit to a maneuver. This work formulates this information-action tradeoff as a belief-space planning problem for conjunctions between a maneuverable spacecraft and an unmaneuverable secondary object. The planner represents the uncertain orbital states as Gaussian beliefs and uses a chance-constrained belief-space Monte Carlo tree search framework to reason over possible future tracking updates before time of closest approach (TCA). A terminal chance constraint limits the probability of reaching TCA above a prescribed collision-risk threshold, allowing the planner to wait for informative tracking while intervening when deferral becomes too risky. We evaluate the approach on eight historical conjunctions from NASA's Conjunction Assessment Risk Analysis dataset. By varying the secondary-object measurement quality and tracking cadence, we generate a total of 96 distinct evaluation scenarios. Across the evaluated conditions, the planner reaches TCA without maneuvering in approximately 40% of episodes while maintaining no terminal collision-risk violations. In contrast, fixed-time rule-based maneuver policies resolve more encounters without maneuvering when intervention is deferred closer to TCA, but at the expense of increasing terminal risk violations. The fraction of episodes reaching TCA without maneuvering depends strongly on tracking quality and measurement cadence, ranging from 76% under accurate, frequent measurements to approximately 18%-20% under the poorest tracking conditions. These results show that tracking quality and frequency are not only inputs to collision-risk estimation: they can determine when intervention becomes necessary.
Comments19 pages, 5 figures, 3 tables, Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference 2026
