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

arXiv: https://arxiv.org/abs/2406.16873 · HTML: https://mlsanity.com/paper/2406.16873/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 185 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [Particle Filter Networks with Application to Visual Localization](https://mlsanity.com/paper/1805.08975/) (2018)
- [Particle Filter Recurrent Neural Networks](https://mlsanity.com/paper/1905.12885/) (2019)
- [Artificial Intelligence for Long-Term Robot Autonomy: A Survey](https://mlsanity.com/paper/1807.05196/) (2018)
- [Unsupervised Learned Kalman Filtering](https://mlsanity.com/paper/2110.09005/) (2021)
- [End-To-End Semi-supervised Learning for Differentiable Particle Filters](https://mlsanity.com/paper/2011.05748/) (2020)
- [Improving GNSS Positioning using Neural Network-based Corrections](https://mlsanity.com/paper/2110.09581/) (2021)
- [Convolutional Neural Network for Multipath Detection in GNSS Receivers](https://mlsanity.com/paper/1911.02347/) (2019)
- [Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas](https://mlsanity.com/paper/2204.14001/) (2022)
- [A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction](https://mlsanity.com/paper/2006.09067/) (2020)

## Built on by (most cited first, 4 of 4)

- [Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset from Multisource Observations](https://mlsanity.com/paper/2411.06202/) (2024)
- [GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning](https://mlsanity.com/paper/2501.02352/) (2025)
- [VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification](https://mlsanity.com/paper/2504.10556/) (2025)
- [Satellite Connectivity Prediction for Fast-Moving Platforms](https://mlsanity.com/paper/2508.00877/) (2025)

## 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.