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
本研究提出自适应合成协方差阈值的低轨卫星 conjunction 筛选方法，结合二分法高度扫描，可大幅减少候选对数量且不遗漏近距离接近目标，提升筛选效率。
AI 中文摘要
随着轨道天体数量的增长，conjunction 筛选需要搜索更多天体对以识别潜在碰撞风险。现有流程采用近地点过滤器等早期筛选器，消除轨道高度范围无法实现近距离接近的天体对。然而，这些筛选器对所有天体应用相同的固定阈值，对轨道约束良好的天体而言会导致筛选过于保守，而对轨道预测不确定性较大的天体则可能筛选不足。本研究提出一种感知不确定性的 conjunction 筛选方法，将单一固定近地点阈值替换为由传播径向不确定性推导的天体特定阈值。所得阈值随天体和预测时间变化，当传播不确定性较小时阈值变窄，不确定性增大时阈值变宽。我们将该自适应筛选规则与二分法高度扫描相结合，使生成的异构区间能以对数线性搜索复杂度在目录规模下高效评估。我们采用空间跟踪网通用摄动（GP）目录中的 33791 个天体进行评估，使用前 8 个公开星历快照和 7 天预测 horizon。该自适应筛选器保留了 1.628 亿个候选对，相较于固定 50 km 近地点阈值，候选集减少了 28.78%，相较于暴力全对筛选减少了 71.48%。它保留了两个独立传播参考集中识别的所有近距离接近目标。在测试的最大目录规模下，二分法扫描比穷举区间评估快 27 倍，同时返回相同的候选集。这些结果表明，感知不确定性的筛选可大幅减少传递给下游 conjunction 分析的候选负担，为管理日益庞大且异构的轨道天体群提供了可扩展路径。
英文摘要
As orbital populations grow, conjunction screening must search more object pairs for potential collision risks. Existing pipelines use early-stage filters, such as an apsis filter, to eliminate pairs whose orbital altitude ranges cannot produce a close approach. However, these filters apply the same fixed threshold to every object, making the screening unnecessarily conservative for well-constrained objects while potentially insufficient for objects with greater orbit-prediction uncertainty. In this work, we present an uncertainty-aware conjunction screening method that replaces a single fixed apsis threshold with object-specific thresholds derived from propagated radial uncertainty. The resulting thresholds vary across both objects and prediction time, becoming narrower when propagated uncertainty is small and wider when uncertainty grows. We pair this adaptive screening rule with a binary-search altitude sweep that makes resulting heterogeneous intervals efficient to evaluate at catalog scale with log-linear search complexity. We evaluate the method on 33,791 objects from the Space-Track General Perturbations (GP) catalog using the preceding eight public ephemeris snapshots and a seven-day prediction horizon. The adaptive sieve retains 162.8 million candidate pairs, reducing the candidate set by 28.78% relative to a fixed 50 km apsis threshold and by 71.48% relative to brute-force all-pairs screening. It retains all close approaches identified in two independently propagated reference sets. At the largest tested catalog size, the binary-search sweep is 27 times faster than exhaustive interval evaluation while returning the same candidate set. These results demonstrate that uncertainty-aware screening can substantially reduce the candidate burden passed to downstream conjunction analysis, providing a scalable path toward managing increasingly large and heterogeneous orbital populations.
