Alinda Mashiku, NASA; Lauri Newman, NASA; Dolan Highsmith, The Aerospace Corporation
Keywords: Artificial Intelligence, Neural Networks, Collision Avoidance, Decision Support, Risk Assessment
Abstract:
To ensure the continued and sustainable use of Low Earth orbit (LEO) and beyond, in general, reliable and effective collision prediction between space objects is critical as the orbital environment faces unprecedented growth. Space Situational Awareness (SSA) for commercial and government missions now confronts rapidly expanding satellite populations ranging from small, potentially less agile CubeSats to large constellations, each presenting unique conjunction assessment challenges and collision risks in the orbital regimes. With space object catalogs expected to increase tremendously and conjunction events becoming more frequent and complex, traditional risk assessment methods may face significant scalability limitations. NASA’s Conjunction Assessment Risk Analysis (CARA) program has conducted comprehensive research utilizing over 450,000 Conjunction Data Messages (CDM) from 2015-2018 to evaluate whether artificial intelligence and machine learning capabilities could enhance or augment collision avoidance decision-making faster by performing conjunction assessment with increased certainty using CDM data available early in event timelines. In essence, can these approaches enable rapid, reliable decision-making for collision avoidance within the critical 7-day identification window, incorporating expected or predicted information measures from anticipated new observations that could provide a bounded solution space. The research employed multiple Artificial Intelligence/Machine Learning (AI/ML) methodologies including such as supervised learning, unsupervised clustering techniques, Fuzzy Inference Systems, Deep Neural Networks, and Long Short-Term Memory models to identify risk-associated patterns during a conjunction as well as analyze the temporal evolution of defined risk parameters. We investigated statistical and information theory parameters beyond traditional probability of collision calculations, developing adaptive models for varying CDM availability, and implementing sophisticated time-series analysis approaches. The comprehensive evaluation revealed that AI/ML solutions undertaken have revealed several challenges and constraints that need to be overcome for operational risk assessment applications. The study’s findings highlight fundamental challenges including data scarcity, the stochastic nature of orbital mechanics, model interpretability requirements, and the critical need for explainable AI approaches that can meet the high-reliability standards essential for space operations decision-making.
Date of Conference: September 16-19, 2025
Track: Conjunction/RPO