A Novel AI Temporal-Spatial Analysis Approach for GNSS Localization Propagation Error Source Recognition

Tong, Kit Lun, Ren, Yi, Shi, Xin, Chen, Zhaohui and Zhang, Xu (2025) A Novel AI Temporal-Spatial Analysis Approach for GNSS Localization Propagation Error Source Recognition. In: 2025 IEEE 102nd Vehicular Technology Conference, VTC 2025-Fall - Proceedings. IEEE Vehicular Technology Conference . The Institute of Electrical and Electronics Engineers (IEEE), CHN. ISBN 9798331503208

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Abstract

Global navigation satellite systems (GNSS) error source analysis is crucial for identifying factors that affect the accuracy of positioning, navigation, and timing services (PNT). Detecting and correcting these factors is essential for enhancing overall service accuracy. Traditional methods primarily focus on surface-level receiver output data, which may overlook underlying factors. Additionally, analyzing daily generated data is expensive and requires advanced proficiency. This research uses a novel temporal-spatial analysis approach to analyze GNSS error sources with artificial intelligence (AI) model support. We develop a noise segments dataset categorized into six types, with a particular focus on ionospheric disclosure, a deeper-level receiver data calculating PNT result. By applying clustering combined with a z-score normalization filter (ZFilter), we identify highly consistent noise segments in daily data, which aids in understanding potential causes. We then employ a multi-model deep learning approach to classify the noise segments, as opposed to relying on a single baseline model. Additionally, we experiment with semi-supervised learning through pseudo-labeling to improve classification performance. Our experiments show that our classifier achieves approximately 84% accuracy in identifying the noise segments.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2025 IEEE.
Uncontrolled Keywords:	clustering,deep learning,gnss error source,pnt,computer science applications,applied mathematics,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1706
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	10 Apr 2026 15:30
Last Modified:	10 Apr 2026 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102753
DOI:	10.1109/VTC2025-Fall65116.2025.11310593

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