Early Prediction of Satellite Collision Probability Using a Hybrid TCN-Transformer Model for a CDM-Based Conjunction Analysis Framework
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Abstract
The paper presents a learning-based framework for early prediction of satellite conjunction risk by estimating the probability of collision (PoC) expected in subsequent Conjunction Data Messages (CDMs).
Reality Card
The proposed hybrid TCN-Transformer model enables earlier and more consistent operational risk evaluation for LEO satellite conjunctions.
The framework utilizes an enriched conjunction dataset to train the model, improving the prediction of collision risk.
The study may face challenges in reproducibility due to the complexity of orbital dynamics and the sensitivity of PoC to covariance evolution.
Paper to code
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