What does this research mean for the field?

The ISO-AI Lite framework provides an explainable AI-based approach for satellite collision risk assessment and manoeuvre planning in Low Earth Orbit by integrating TLE datasets, the SGP4 orbital propagation model, and both analytical and Monte Carlo collision probability estimation methods. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to create an explainable AI framework for evaluating satellite collision risks and planning maneuvers in low Earth orbit.

March 28, 2026Open Access

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Key Points

The research aims to create an explainable AI framework for evaluating satellite collision risks and planning maneuvers in low Earth orbit.
Developed a system for assessing satellite collision risks
Utilized Two-Line Element (TLE) datasets for data acquisition
Employed SGP4 model for orbital propagation
Calculated collision probabilities using analytical and Monte Carlo methods
Facilitated effective assessment of collision risks in low Earth orbit
Enhanced planning of satellite maneuvers to avoid collisions

Abstract

This research work, titled “ISO-AI Lite: An Explainable AI Framework for Satellite Collision Risk Assessment and Manoeuvre Planning in Low Earth Orbit” was primarily conceived, designed, and implemented by Niveditha A. The author was responsible for the complete system development, including problem formulation, data acquisition using Two-Line Element (TLE) datasets, orbital propagation using the SGP4 model, and the implementation of collision probability estimation using both analytical and Monte Carlo methods.

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Key Points

Abstract

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