pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons

Artificial intelligence (AI) is revolutionizing numerous fields, with increasing applications in Global Navigation Satellite Systems (GNSS) positioning algorithms in intelligent transportation systems (ITS) via deep learning. However, a significant technological disparity exists as traditional GNSS...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Hu, Runzhi, Xu, Penghui, Zhong, Yihan, Wen, Weisong
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Binding
Deep learning
Global navigation satellite system
Intelligent transportation systems
Machine learning
Open source software
Street canyons
Subsystems
Online Access:Get full text
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Summary:Artificial intelligence (AI) is revolutionizing numerous fields, with increasing applications in Global Navigation Satellite Systems (GNSS) positioning algorithms in intelligent transportation systems (ITS) via deep learning. However, a significant technological disparity exists as traditional GNSS algorithms are often developed in Fortran or C, contrasting with the Python-based implementation prevalent in deep learning tools. To address this discrepancy, this paper introduces pyrtklib, a Python binding for the widely utilized open-source GNSS tool, RTKLIB. This binding makes all RTKLIB functionalities accessible in Python, facilitating seamless integration. Moreover, we present a deep learning subsystem under pyrtklib, which is a novel deep learning framework that leverages pyrtklib to accurately predict weights and biases within the GNSS positioning process. The use of pyrtklib enables developers to easily and quickly prototype and implement deep learning-aided GNSS algorithms, showcasing its potential to enhance positioning accuracy significantly.
ISSN:2331-8422