用随机几何优化地面传感器，高效追踪低轨太空目标
Time Invariant Sensor Tasking for Catalog Maintenance of LEO Space objects using Stochastic Geometry
- 基于泊松点过程构建时不变追踪框架
- 提升多目标同时跟踪的可见性与效率
- 适合空间态势感知与轨道安全决策者
有限数量的地基传感器对低地球轨道（LEO）空间物体的目录维护构成重大挑战。本文提出一种时不变追踪与监视方法，通过最优调度地面传感器来最大化可观测空间物体数量。该方法利用随机几何中的泊松点过程理论，系统分析可见性模式，提升多目标并行跟踪效率。研究成果有助于提升空间操作决策水平，支持低轨空间安全与可持续发展。
原文摘要 · Abstract (English)
Catalog maintenance of space objects by limited number of ground-based sensors presents a formidable challenging task to the space community. This article presents a methodology for time-invariant tracking and surveillance of space objects in low Earth orbit (LEO) by optimally directing ground sensors. Our methodology aims to maximize the expected number of space objects from a set of ground stations by utilizing concepts from stochastic geometry, particularly the Poisson point process. We have provided a systematic framework to understand visibility patterns and enhance the efficiency of tracking multiple objects simultaneously. Our approach contributes to more informed decision-making in space operations, ultimately supporting efforts to maintain safety and sustainability in LEO.
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