Predicting Orbital State Vector of Satellites Using Time-Series Neural Networks

Prediction of satellite orbital position is a critical requirement for all satellite ground stations. In this paper, a new viewpoint for predicting orbital position of satellites is presented. In contrast to traditional methods which are based on Kepler's law, the proposed method, is solely based on past observation of a given satellite. In contrast to traditional algorithms which have neglected some of the orbital perturbations, the most important feature of this method is considering all orbital perturbations by using real data. TLEs (Two Line Element sets) are the most available real data and are used in this research as the main data source. Using the capability of neural networks for time series prediction over available data, results in a fast and accurate orbital position predictor. The comparison between the output of our proposed method, SPG4 (Simplified General Perturbation version 4) propagator and real orbital position of a given satellite, shows the effectiveness of this algorithm.