Enhancing Geomagnetic Navigation with PPO-LSTM: Robust Navigation Utilizing Observed Geomagnetic Field Data

Enhancing Geomagnetic Navigation with PPO-LSTM: Robust Navigation Utilizing Observed Geomagnetic Field Data

Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation....

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Bibliographic Details
Main Authors: Xiaohui Zhang, Wenqi Bai, Jun Liu, Songnan Yang, Ting Shang, Haolin Liu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
deep reinforcement learning
partially observed Markov decision process
bionic geomagnetic navigation
proximal policy optimization
Online Access:https://www.mdpi.com/1424-8220/25/12/3699
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Summary:Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation. Since vehicles can only observe the geomagnetic field along their traversed paths, they must rely on incomplete information to infer the navigation strategy; therefore, we formulate the navigation problem as a partially observed Markov decision process (POMDP). To address this POMDP, we employ proximal policy optimization with long short-term memory (PPO-LSTM), a deep reinforcement learning framework that captures temporal dependencies and mitigates the effects of noise. Using real-world geomagnetic data from the international geomagnetic reference field (IGRF) model, we validate our approach through experiments under noisy conditions. The results demonstrate that PPO-LSTM outperforms baseline algorithms, achieving smoother trajectories and higher heading accuracy. This framework effectively handles the uncertainty and partial observability inherent in geomagnetic navigation, enabling robust policies that adapt to complex gradients and offering a robust solution for geospatial navigation.
ISSN:1424-8220

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