Autonomous orbit determination for lunar navigation constellation using low Earth orbit satellites.

Saved in:
Bibliographic Details
Title: Autonomous orbit determination for lunar navigation constellation using low Earth orbit satellites.
Authors: He, Bei1,2 (AUTHOR), Xu, Tianhe1,2 (AUTHOR) thxu@sdu.edu.cn, Wang, Dixing1,2 (AUTHOR), Liu, Yangfan1,2,3 (AUTHOR)
Source: Advances in Space Research. Mar2026, Vol. 77 Issue 6, p6659-6674. 16p.
Subjects: Orbit determination, Low earth orbit satellites, Navigation, Synchronization, Lunar orbit, Lunar exploration, Moon, Artificial satellites in navigation
Abstract: As human space exploration extends into cislunar and deep space regions, the need for autonomous navigation and high-precision time synchronization independent of ground-based systems is becoming increasingly critical. This study proposes a space-based autonomous Positioning, Navigation, and Timing (PNT) system architecture for the cislunar environment. The system integrates a network of Low Earth Orbit (LEO) satellites and continuously tracks a hybrid lunar constellation via Ka-band Inter-Satellite Link (ISL). The lunar constellation consists of satellites deployed in both Elliptical Lunar Frozen Orbit (ELFO) and Distant Retrograde Orbit (DRO), enabling full coverage of the Moon. Simulation results demonstrate orbit determination accuracies better than 5 m for ELFO and 15 m for DRO satellites, and time synchronization precision better than 5 ns. The proposed architecture supports continuous positioning services across the lunar surface, with positioning accuracy better than 3 m. These results confirm the feasibility and effectiveness of a fully space-based autonomous PNT system to support future lunar exploration. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Be the first to leave a comment!
You must be logged in first