Intelligent Propagation for an Inclined Geosynchronous Orbit Using Mean Elements

High-accuracy propagation for an inclined geosynchronous orbit (IGSO) is of great importance to region navigation enhancement. Mean orbital elements enable accurate and efficient long-term propagation, particularly for large-scale computations. However, due to errors in transformations between mean and oscillating elements and mean dynamics, the actual orbit drifts from the propagated IGSO. In order to increase the orbit accuracy, an intelligent propagation method for the IGSO with respect to the mean elements is developed. Transformation models between the mean and oscillating elements of the IGSO under a precise dynamic model are derived. The orbital perturbations on the IGSO with respect to non-singular elements are decomposed into secular and periodic terms based on an averaged method. Mean dynamic errors degrading long-term propagation and errors in short-period terms affecting transformation are identified and separated. To neutralize the errors and improve the long-term propagating accuracy, a neural network-based propagation method for the IGSO is established. The transforming and propagating errors are fitted and predicted using two individual deep neural networks. Training methods for both networks are constructed. Numerical simulations validate the performance of the proposed NN-based transformation method and the intelligent propagation method for the IGSO.