A Hybrid Approach to Enhanced SGP4 for Galileo Constellations

An up-to-date catalog of residents space objects orbiting Earth requires a critical balance between computational efficiency and orbital prediction precision. This work presents HSGP4, a hybrid orbit propagator specifically tailored for Galileo-type orbits that enhances the classical SGP4 analytical model using Artificial Neural Networks. The methodology centers on a non-invasive hybridization process that utilizes high-fidelity pseudo-observations to forecast SGP4 error residuals. A core contribution is the introduction of the Hybrid Two-Line Element format, which encapsulates neural model parameters alongside traditional orbital elements, ensuring seamless integration with existing catalog infrastructure. The development process involved comprehensive Exploratory Data Analysis and sensitivity analysis, which identified the argument of latitude as the most influential variable for correcting SGP4 errors in the MEO region. To ensure statistical robustness, a hierarchical selection strategy was implemented. This reduced an exhaustive search space of 32,256 candidate architectures to a final subset of optimized configurations. Validated against a decade of TLE data, the results confirm that HSGP4 effectively captures missing dynamic patterns and significantly improves ephemeris accuracy. By forecasting SGP4 error residuals, this hybrid approach provides a high-fidelity correction layer. It compensates for the limitations of analytical theories without requiring complex numerical integration.