Refining satellite trajectories with celestial body features using neural networks

Satellite orbit propagation involves predicting a satellite’s future position and velocity based on initial conditions. Traditional physical models, such as SGDP4, simplify the forces that act on the satellite to achieve high computational efficiency at the cost of reduced prediction accuracy, especially over longer time intervals where error accumulates. More sophisticated models like HPOP offer improved accuracy at the cost of high prediction times, rendering them unusable for realtime long-term predictions. Recent advancements have introduced machine learning techniques to refine these predictions and reduce errors. However, they often lack an analysis of model design choices, such as input feature selection and architectural configurations. Existing models do not incorporate features related to the state of celestial bodies, such as the positions of the Moon or Sun, which can influence the satellite’s trajectory. This paper proposes a novel model that integrates such features at both the initial time and throughout the prediction interval, leveraging their potential impact on the orbit of the satellite. The model is based on a neural network architecture employing GRU layers for encoding sequential data about the celestial conditions. Our results demonstrate that the inclusion of these sequential features significantly reduces prediction errors. Additionally, we have evaluated a variety of design choices such as independent sub-models for specific spatial coordinates and time intervals, further enhancing performance. These innovations lead to substantial improvements in both short- and long-term orbit predictions, providing a more robust and accurate alternative for satellite orbit propagation. • Hybrid model integrates SGDP4 with neural networks for improved orbit predictions. • Celestial body positions included as sequences are key features to enhance accuracy. • Separate models per coordinate and time window boost overall predictive performance. • Improved long-term forecast (10h+) while matching or exceeding short-term accuracy.