Fusion of a machine learning approach and classical orbit predictions

Abstract

Orbit prediction accuracy is often limited by the underlying physics-based models and estimation methods. Instead of advancing the state-of-the-art from those two aspects, we have recently developed a physics-based machine learning (ML) methodology to discover useful information from historical orbit predictions errors. This paper is posed to answer the next question: how can we fuse the ML approach with classical orbit predictions? The classical method provides valuable and reliable information and should not be abandoned. Using the extended Kalman filter (EKF) as the orbit estimation and prediction method, this paper presents an innovative fusion strategy to incorporate the ML output to the conventional framework. We define the pseudo-physical meaning of the ML output and derive an analytical model for fusion. Using a simulation-based space catalog environment, the paper demonstrates that the proposed fusion strategy can improve both the orbit prediction accuracy and precision. Discussions and insights are presented including the possible causes for some unsatisfying results. Although EKF is used in this paper, the design of the fusion strategy for the ML approach can be generalized to systems with different orbit estimation and prediction methods.