Robust Real-Time Orbit Determination After GNSS Outages via Uncertainty-Aware Predicted-Orbit Constraints

Accurate and continuous onboard real-time orbit determination (RTOD) is essential for Low Earth Orbit (LEO) satellites, but temporary interruptions of onboard Global Navigation Satellite Systems (GNSS) observations can degrade orbit continuity and delay filter re-convergence. Although predicted-orbit constraints can accelerate RTOD re-convergence after GNSS outages, their effectiveness largely depends on a realistic stochastic representation of the accumulated prediction uncertainty. Here, we develop a prior-state covariance model to characterize the time-dependent prediction uncertainty and provide uncertainty-consistent predicted-orbit constraints after observation recovery. Using onboard GNSS data from LuTan-1A as the primary dataset, orbit prediction residuals were analyzed through marginal-distribution analysis, envelope statistics, and a Mahalanobis-distance-based goodness-of-fit test. Results indicate that prediction errors are consistent with an approximate Gaussian model over the investigated prediction interval, supporting construction of a time-continuous prior-state covariance matrix. The proposed model was subsequently evaluated using a temporally independent LT-1A dataset. For GNSS interruptions of 15–90 min, the proposed model achieved average re-convergence times of 0.59–1.53 min, compared with 1.41–3.90 min for the state transition matrix (STM)-propagated covariance strategy and 9.18–19.12 min for the loose-prior strategy, while maintaining post-convergence 3D position RMS errors of 12.26–13.83 cm. Relative to STM propagation, average re-convergence time decreased from 1.41 to 0.59 min for the 15 min interruption and from 3.12 to 1.46 min for the 60 min interruption. Additional validation using GRACE-FO C, Sentinel-3A, and Sentinel-6A demonstrated applicability across different orbital and GNSS observation conditions. Overall, these results demonstrate that realistic characterization of prediction uncertainty can substantially improve RTOD re-convergence while preserving post-convergence orbit accuracy.