Early Prediction of Satellite Collision Probability Using a Hybrid TCN-Transformer Model for a CDM-Based Conjunction Analysis Framework

A study posted to arXiv proposes a hybrid Temporal Convolutional Network (TCN)-Transformer model to predict the probability of collision (PoC) expected in the next Conjunction Data Message (CDM) update for the same close approach event, aiming to give satellite operators earlier and more consistent risk evaluation. The framework, applied to CDMs received and analyzed within TÜBİTAK UZAY, first uses an Unscented Transform-based propagation and backpropagation framework to assess the sensitivity of the collision risk metric to CDM parameters, then applies Principal Component Analysis to numerical CDM parameters to identify the features most relevant to PoC variation. The authors state the approach targets low Earth orbit (LEO) conjunctions, where the growth of operational satellites and orbital debris has increased close approach frequency and where electric-propulsion satellites face added time constraints on collision avoidance planning.

arXiv:2609.13191v1 Announce Type: new Abstract: The rapid expansion of operational satellites and orbital debris has increased the frequency of close approach events in low Earth orbit LEO , creating a higher operational burden for satellite operators. This problem is especially critical for satellites using electric propulsion, where low-thrust maneuver capability imposes additional time constraints on collision avoidance planning. In current practice, Conjunction Data Messages CDMs provide relative state, covariance, miss distance, time of closest approach, and probability of collision PoC information for conjunction assessment. However, the nonlinear propagation of orbital uncertainties and the sensitivity of PoC to covariance evolution make the interpretation of sequential CDMs challenging. This study proposes a learning-based framework for early prediction of satellite conjunction risk by estimating the PoC expected in the subsequent CDM update of the same close approach event. In the proposed methodology, an Unscented Transform-based propagation and backpropagation framework is first used to evaluate the sensitivity of the collision risk metric to CDM parameters. In addition, Principal Component Analysis is applied to the numerical CDM parameters to identify the features most relevant to PoC variation. The results obtained from the sensitivity analysis and PCA are then used to justify the selected raw CDM parameters and to construct derived metrics representing relative motion, encounter geometry, and covariance-related uncertainty. Using the resulting sequential enriched conjunction dataset, a hybrid Temporal Convolutional Network TCN -Transformer model is trained to learn the temporal evolution of conjunction risk. The framework is applied to CDMs received and analyzed within T\"UB\.ITAK UZAY, demonstrating its potential for earlier and more consistent operational risk evaluation for LEO satellite conjunctions.