Time Invariant Sensor Tasking for Catalog Maintenance of LEO Space objects using Stochastic Geometry

2025-06-29

AI Summary

To address the challenges of limited ground-based sensor resources and unstable long-term task allocation in low Earth orbit (LEO) space object catalog maintenance, this paper proposes a time-invariant ground sensor scheduling method. Leveraging stochastic geometry—specifically, modeling spatial object visibility distributions via Poisson point processes—we establish a global observability analysis framework. Integrating orbital dynamics with ground station observational constraints, we design an optimal sensor pointing algorithm enabling efficient, simultaneous multi-object tracking. Simulation results demonstrate that the method significantly increases the number of observable objects per unit time and improves catalog update efficiency, thereby enhancing the continuity and robustness of LEO space situational awareness. The approach provides a scalable technical pathway for safe and sustainable operations in large-scale LEO environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Optimize ground sensor direction for LEO object tracking
• Maximize tracked objects using stochastic geometry concepts
• Enhance space catalog maintenance efficiency and safety

Innovation

• Time-invariant tracking using stochastic geometry
• Optimal ground sensor direction for LEO objects
• Poisson process enhances multi-object tracking efficiency