Bayesian Integration for Deep-Space Navigation with GNSS Signals

Recent advancements in spaceborne receiver technology have extended the application of Global Navigation Satellite System (GNSS)-based navigation systems to space missions. However, the actual availability and usability of GNSS signals in deep-space is still questionable, lacking experimental evidence. The Lunar GNSS Receiver Experiment (LuGRE) is a joint NASA-Italian Space Agency (ASI) payload aiming to showcase GNSS-based Positioning, Navigation and Timing (PNT) during its transfer orbit to the Moon. Operating without direct interface with on-board Guidance, Navigation & Control (GNC) subsystems, the LuGRE receiver requires alternative means of aiding to pursue precise Orbit Determination (OD) in the challenging space environment. This paper investigates a custom Trajectory-Aware EKF (TA-EKF) architecture that integrates aiding observations in the form of a pre-mission design of the LuGRE trajectory. Two alternative designs are presented, integrating aiding observations in the observation-domain and state-domain, respectively. The proposed architectures are evaluated by post-processing raw GNSS observables collected in a real-time Hardware-in-the-Loop (HIL) simulation with GNSS Radio Frequency (RF) signals. A comprehensive assessment leveraging Monte Carlo (MC) analyses characterizes the OD performance under aiding observation errors and mismodeling, comparing the TA-EKF models against a standalone Extended Kalman Filter (EKF) solution.