mlsanityData to 7 Oct 2026

A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems

arXiv 2406.16873 ↗First posted 29 Mar 2024eess.SP cs.AI cs.LGcited by 4

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Abstract (arXiv)

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based and they utilize satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in Machine Learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 185 references that are in the map. First posted on arXiv 29 Mar 2024.

Builds on

16 of 185 references.

References · 16

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4 more
  1. 2021Improving GNSS Positioning using Neural Network-based Correctionscited by 10
  2. 2019Convolutional Neural Network for Multipath Detection in GNSS Receiverscited by 2
  3. 2022Machine Learning-Based GPS Multipath Detection Method Using Dual Antennascited by 2
  4. 2020A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake predictioncited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset from Multisource Observationscited by 1
  2. 2025GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learningcited by 1
  3. 2025VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classificationcited by 1
  4. 2025Satellite Connectivity Prediction for Fast-Moving Platformscited by 0