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

TL;DR

This survey reviews recent machine learning techniques applied to enhance GNSS positioning, addressing challenges in traditional methods through diverse ML approaches like deep learning and hybrid models.

Contribution

It provides a comprehensive overview of ML methods for GNSS, highlighting recent advances, applications, and challenges in improving positioning accuracy and robustness.

Findings

ML improves GNSS accuracy in challenging environments

Hybrid ML models enhance signal analysis and anomaly detection

Deep learning enables better multi-sensor data integration

Abstract

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.