Improvement of orbit prediction accuracy using extreme gradient boosting and principal component analysis

High-accuracy orbit prediction plays a crucial role in several aerospace applications, such as satellite navigation, orbital maneuver, space situational awareness, etc. The conventional methods of orbit prediction are usually based on dynamic models with clear mathematical expressions. However, coefficients of perturbation forces and relevant features of satellites are approximate values, which induces errors during the process of orbit prediction. In this study, a new orbit prediction model based on principal component analysis (PCA) and extreme gradient boosting (XGBoost) model is proposed to improve the accuracy of orbit prediction by learning from the historical data in a simulated environment. First, a series of experiments are conducted to determine the approximate numbers of features, which are used in the following machine learning (ML) process. Then, PCA and XGBoost models are used to find incremental corrections to orbit prediction with dynamic models. The results reveal that the designed framework based on PCA and XGBoost models can effectively improve the orbit prediction accuracy in most cases. More importantly, the proposed model has excellent generalization capability for different satellites, which means that a model learned from one satellite can be used on another new satellite without learning from the historical data of the target satellite. Overall, it has been proved that the proposed ML model can be a supplement to dynamic models for improving the orbit prediction accuracy.