Orbit determination pipeline for geostationary objects using physics-informed neural networks

Due to the importance of the geostationary orbit belt, tracking and identification of satellites in such orbits is of pivotal importance for space situational awareness. To address this problem, a comprehensive pipeline for satellite tracking is proposed, starting from raw observations to satellite identification and tracking. The pipeline includes "Stingray", an advanced hardware system with 15 cameras for high-resolution optical observations of the GEO belt. The astrometry component extracts the necessary information for orbit determination, while physics-informed neural networks (PINNs) estimate the object orbits, considering significant dynamics disturbances including third-body perturbations, solar radiation pressure and control maneuvers. The proposed framework is capable of processing the orbit determination of several objects in parallel and is able to automatically re-process all those observations for which the orbit determination previously failed by varying the PINN hyperparameters. Thus, the pipeline results to be accurate and efficient, thus being proper for catalog maintenance.