Establishing a regression baseline for predicting satellite motion

Determining the orbital paths of space objects is a critical task in astronomy. In particular, knowledge of satellite trajectories is essential to avoid costly and hazardous collisions between satellites in space. However, due to the amount and complexity of variables affecting a satellite’s orbit, it is no small feat to accurately predict its position. Moreover, it was only recently that novel alternatives to physics-based models have been proposed, namely machine learning (ML) models that can learn from historical data and make improvements to orbit prediction accuracy. Motivated by the hope that ML models can capture the underlying pattern of satellite orbital trajectories, the goal of this paper is to apply a supervised ML model called non-linear regression, to predict the position and velocity of a single satellite in orbit around the Earth. The study establishes a simple non-linear regression baseline for predicting satellite motion three days in advance, from which more complex ML models can be applied. Obtained forecasts were within acceptable error margins and the overall result shows promise in applying ML to predict satellite motion.