Autonomous Lunar Orbit Determination in Support of a Lunar Positioning System

John R. Bowman, Mark L. Psiaki

Abstract: This study presents a direct comparison of the LiAISON, Relative-Positon, and Star-Occultation autonomous orbit determination methods for spacecraft in Lunar orbit. Recently, there has been much interest in the development of Lunar navigation satellite systems which leverage existing GNSS signals for Lunar orbit determination. This paper presents an alternative approach in which satellites navigate without need of Earth-based signals like those of the Deep Space Network or existing GNSS. The present study develops a high-fidelity truth-model simulation and analyzes Kalman filter results computed using three autonomous orbit determination techniques. The LiAISON method, which uses measurements of range and/or range-rate between a satellite in Lunar orbit and another satellite in a 3-body-dominated orbit, is found to have a steady-state RMS position error magnitude of approximately 2.1 m. The Relative Position method, which uses measurements of the inertial vector between two spacecraft in Lunar orbit, is found to have a steady-state RMS position error magnitude of approximately 4.6 m. The Star Occultation method, which measures the time of occultation or rising of stars over the Lunar limb, is found to have a steady-state RMS position error magnitude of approximately 4.5 m. If one could implement a sufficiently accurate timing solution, satellites that employ one or more of these methods to autonomously determine their orbits could form the backbone of an accurate Lunar Positioning System.
Published in: Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)
September 11 - 15, 2023
Hyatt Regency Denver
Denver, Colorado
Pages: 4016 - 4028
Cite this article: Bowman, John R., Psiaki, Mark L., "Autonomous Lunar Orbit Determination in Support of a Lunar Positioning System," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 4016-4028. https://doi.org/10.33012/2023.19427
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