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Remote Sensing
Volume 17
Issue 15
10.3390/rs17152692
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy

by
Jiaqi Yin
1,2,†,
Feilong Li
3,†,
Ruidan Luo
1,*,
Xiao Chen
1,
Linhui Zhao
1,
Hong Yuan
1 and
Guang Yang
1
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101400, China
3
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2025, 17(15), 2692; https://doi.org/10.3390/rs17152692 (registering DOI)
Submission received: 25 June 2025 / Revised: 26 July 2025 / Accepted: 28 July 2025 / Published: 3 August 2025
(This article belongs to the Special Issue LEO-Augmented PNT Service)
Versions Notes

Abstract

Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research.
Keywords: low Earth orbit; signal of opportunity; deep reinforcement learning; Iridium system; Doppler positioning low Earth orbit; signal of opportunity; deep reinforcement learning; Iridium system; Doppler positioning

Share and Cite

MDPI and ACS Style

Yin, J.; Li, F.; Luo, R.; Chen, X.; Zhao, L.; Yuan, H.; Yang, G. The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy. Remote Sens. 2025, 17, 2692. https://doi.org/10.3390/rs17152692

AMA Style

Yin J, Li F, Luo R, Chen X, Zhao L, Yuan H, Yang G. The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy. Remote Sensing. 2025; 17(15):2692. https://doi.org/10.3390/rs17152692

Chicago/Turabian Style

Yin, Jiaqi, Feilong Li, Ruidan Luo, Xiao Chen, Linhui Zhao, Hong Yuan, and Guang Yang. 2025. "The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy" Remote Sensing 17, no. 15: 2692. https://doi.org/10.3390/rs17152692

APA Style

Yin, J., Li, F., Luo, R., Chen, X., Zhao, L., Yuan, H., & Yang, G. (2025). The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy. Remote Sensing, 17(15), 2692. https://doi.org/10.3390/rs17152692

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