Optimization of orbit prediction strategies for GNSS satellites

The ability to provide precise orbit prediction of Global Navigation Satellite System (GNSS) satellites is essential in a wide range of applications in space geodesy and Earth sciences, including near real-time and real-time GNSS applications, forming broadcast ephemerides, or supporting Satellite Laser Ranging stations in tracking. In this study, we evaluate the impact of orbit modeling strategies on the accuracy of orbit predictions on short (one-day), and long (multi-day) time scales, focusing on the selection of the optimal approach for handling solar radiation pressure (SRP), the effects of including pseudo-stochastic parameters, and the impact of the arc length used for the initial orbit fit. The analysis includes 300 days of predictions in 2021 and the satellites belonging to the GPS, Galileo, GLONASS, BeiDou-3, (BDS-3), and QZSS constellations. Fitting an initial 2-day orbit arc is the optimal solution for all analyzed navigation satellite constellations. When official satellite construction metadata are available, e.g. for Galileo/QZSS, the hybrid strategy of combining both empirical and physical models, i.e. the extended Empirical CODE Orbit Model (ECOM2) with box-wing models, leads to the best results. Otherwise, using only the ECOM2 is a better choice. Finally, the results indicate that for all navigation satellites, the introduction of pseudo-stochastic parameters deteriorates the prediction quality. When using the optimal prediction strategy, the 95th percentiles of the position errors after the 1st/4th/9th day of prediction are equal to 0.09/0.93/4.52, 0.22/1.71/9.69, 0.20/2.19/11.30, 0.23/1.80/9.39, 0.23/2.28/7.78m for GPS-IIF, Galileo FOC, GLONASS-M, BDS-3 CAST, and QZSS, respectively.