Session F3a: Lunar Positioning, Navigation, and Timing

Advancing Autonomous Navigation: GNSS-Based Orbit Determination in Deep Space
Oliviero Vouch, Andrea Nardin, Alex Minetto, Simone Zocca, Fabio Dovis, Department of Electronics and Telecommunications (DET), Politecnico di Torino; Lauren Konitzer, Joel J.K. Parker, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA); Fabio Bernardi, Simone Tedesco, Samuele Fantinato, Qascom s.r.l.
Alternate Number 2

Peer Reviewed

In current space exploration endeavors, navigation and maneuvering of space vehicles mostly rely on ground-based assets. While technologies like Radio Frequency (RF) tracking via Deep Space Networks (DSNs) and Direct-to-Earth (DTE) links, combined with sophisticated off-board processing algorithms, can enable accurate and precise Orbit Determination (OD), this dependence brings about downsides. Operational costs escalate, and managing multiple missions becomes increasingly challenging due to constraints on ground segment resources. With the challenges of the deep-space exploration roadmap being on the horizon, enhanced spacecraft autonomy for navigation tasks becomes imperative. Global Navigation Satellite Systems (GNSSs) appear to be a crucial asset, and there is a growing interest in leveraging GNSS systems for onboard autonomous navigation during transfer orbits and to assist in complex maneuvers. However, the use of GNSS beyond the Space Service Volume (SSV) limits faces significant hurdles, and the actual availability and usability of GNSS signals in deep space is still questionable, lacking experimental evidence. The Lunar GNSS Receiver Experiment (LuGRE), a joint effort between NASA and the Italian Space Agency (ASI), aims to showcase multi-GNSS-based Positioning, Navigation, and Timing (PNT) in cis-lunar space and at Moon altitudes. Aligning to the LuGRE operational assumptions, this paper sets out to demonstrate the feasibility of GNSS-based OD when the receiver is designed to operate as a self-contained module independently of the spacecraft Guidance, Navigation, and Control (GNC) subsystems. The proposed methodology considers a custom Extended Kalman Filter (EKF)-based architecture that integrates aiding observations in the form of a pre-mission spacecraft trajectory design. In particular, two Trajectory-Aware EKF (TA-EKF) designs are foreseen that incorporate aiding observations in the observation and state domains. These architectures offer several advantages, including enabling precise GNSS-based OD with minimal prior process information and simplifying dynamical models to relax computational resource constraints. Raw GNSS observables collected by the LuGRE receiver from simulated RF signals relative to an Earth-Moon transfer orbit (MTO) segment are post-processed to showcase the performance of the TA-EKF architectures compared to a standalone EKF solution. Extensive Monte Carlo (MC) analyses assess OD performance under various conditions, including aiding observation errors and mismodeling. Findings highlight the effectiveness of the proposed TA-EKF architectures in enhancing GNSS-based OD for deep-space missions. By reducing the reliance on ground-based assets and empowering autonomous navigation, this research contributes to advances in deep-space exploration.