Extending the hybrid methodology for orbit propagation by fitting techniques

Abstract The hybrid methodology for orbit propagation is a technique that allows improving the accuracy of any propagator for predicting the future trajectory of a satellite or space-debris object in orbit around the Earth. It is based on modeling the error of the base propagator to be enhanced. Both statistical time-series forecasting methods and machine-learning techniques can be used for that purpose. The standard procedure for developing a hybrid orbit propagator requires some initial control data, that is, a set of precise ephemerides corresponding to either real observations or accurately computed pseudo-observations, from which to model the base-propagator error dynamics. It also needs tuning the time-series forecaster from those control data. We propose an improvement to the hybrid methodology for orbit propagation, based on fitting new hybrid propagators from others previously developed for nearby initial conditions, which avoids the need for both the control data and the tuning process, and achieves comparable results.