A Customized EKF model for GNSS-based Navigation in the Harsh Space Environment

The extension of the Global Navigation Satellite System (GNSS) Space Service Volume (SSV) is of utmost relevance to afford enhanced autonomy in navigation, guidance, and control of space missions. Pioneering studies have shown the feasibility of using terrestrial GNSS signals in space applications, supporting Orbit Determination and Time Synchronization (ODTS) during Earth-Moon transfer orbits (MTOs) and lunar landings. However, non-terrestrial applications face challenges due to compromised signal availability at high altitudes, thus requiring advanced receiver architectures coupled with external aiding data. This paper presents a customized Bayesian filter, the Trajectory-Aware Extended Kalman Filter (TA-EKF), specifically designed for GNSS navigation along MTOs. The proposed filter architecture integrates aiding information, such as the planned mission orbital trajectory, to speed up filter convergence and achieve highly accurate positioning solutions. The performance of the TA-EKF is evaluated through simulations of MTO mission scenarios supported by Monte Carlo analyses, and it is compared against a standalone EKF.