AI-Enhanced Orbit Determination: The AI4POD Framework

In recent years, the field of space situational awareness (SSA) has gained increasing attention, driven by the rapid rise in both active satellites and orbital debris. Therefore, being able to predict the orbit of a resident space object (RSO) as accurately as possible is more critical than ever in order to reduce collision risks and to preserve the orbital environment. However, incomplete knowledge of debris geometry, uncertain object characteristics, or simplified force models can cause prediction errors which exceed orders of several kilometers within just a few days, making it useless for reliable collision avoidance operations. Using modern Machine Learning (ML) algorithms can enhance prediction accuracy by addressing these challenges as recent studies have shown. In this context we present Artificial Intelligence for Precise Orbit Determination (AI4POD), a Python package that is designed to simplify the integration of ML-algorithms within the orbit prediction and determination process. AI4POD is structured as a comprehensive toolbox that includes a high-fidelity force model, various measurement functions, and classical orbit determination (OD) algorithms such as the batch least-squares estimation method. This integrated approach allows users to combine traditional orbit simulations with data-driven approaches to improve accuracy and to extend the predictability horizon. Based on this catalog, several approaches from artificial intelligence (AI) shall be tested in the future. Inspired by already proposed methodologies we are generating a training set of historical tracking data along with their corresponding orbit determinations using the AI4POD toolbox. Several machine learning algorithms will be explored to learn the nonlinear prediction errors, aiming to compensate for unmodeled or uncertain factors such as incomplete knowledge of satellite geometry or environmental conditions.