Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
This work addresses the problem of Space Situational Awareness for collision avoidance and debris mitigation, offering a faster and more reliable method compared to conventional propagators, though it appears incremental as it builds on existing machine learning and time-series techniques.
The paper tackles the challenge of accurate orbit prediction for space objects by proposing a machine learning algorithm that uses past positions and environmental variables to forecast state vectors, achieving low positioning errors at a low computational cost.
The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is crucial to anticipate the position and velocity of space objects, for collision avoidance and space debris mitigation. When performing Orbit Prediction (OP), it is necessary to consider the impact of non-conservative forces, such as atmospheric drag and gravitational perturbations, that contribute to uncertainty around the future position of spacecraft and space debris alike. Conventional propagator methods like the SGP4 inadequately account for these forces, while numerical propagators are able to model the forces at a high computational cost. To address these limitations, we propose an orbit prediction algorithm utilizing machine learning. This algorithm forecasts state vectors on a spacecraft using past positions and environmental variables like atmospheric density from external sources. The orbital data used in the paper is gathered from precision ephemeris data from the International Laser Ranging Service (ILRS), for the period of almost a year. We show how the use of machine learning and time-series techniques can produce low positioning errors at a very low computational cost, thus significantly improving SSA capabilities by providing faster and reliable orbit determination for an ever increasing number of space objects.