A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems
It reviews incremental improvements for GNSS users in fields such as transportation and emergency services.
This survey examines how machine learning techniques can address limitations of traditional model-based methods in Global Navigation Satellite Systems (GNSS) for applications like navigation and mapping, providing a comprehensive overview of recent advances and challenges.
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.