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

TL;DR

This paper introduces a stochastic geometry-based method for optimizing ground sensor deployment to achieve time-invariant tracking of LEO space objects, improving catalog maintenance efficiency.

Contribution

It presents a novel framework using Poisson point processes for sensor tasking, enhancing visibility understanding and tracking of multiple space objects in LEO.

Findings

Maximized expected number of tracked objects using the proposed method

Provided a systematic approach to analyze visibility patterns

Enhanced decision-making for space object catalog maintenance

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.