A VMD-SVM Method for LEO Satellite Orbit Prediction with Space Weather Parameters

Abstract

The technology of satellite orbit prediction (OP) is crucial in space engineering. However, it is difficult to precisely predict medium and long-term orbit for the low Earth-orbit (LEO) satellites because of time-varying space weather and inaccurate atmospheric density models. To address the problem, a novel intelligent OP method based on the variational mode decomposition-support vector machine (VMD-SVM) framework is presented. First, the concept of a pseudo-drag coefficient is defined, transforming the OP problem into a pseudo-drag coefficient prediction problem. Second, the relationship between space weather parameters and the pseudo-drag coefficient is analyzed using the VMD method, from which a strong correlation is shown. Furthermore, an SVM model combined with space weather characteristic parameters is employed to predict the pseudo-drag coefficient, significantly improving the precision of OP when further integrated into the orbital dynamics model. Experiments with data from engineering applications show that VMD-SVM medium and long-term OP technology is practical and effective.