A Portfolio of Machine Learning-based GNSS LOS/NLOS Classification in Urban Environments

GNSS signal quality is severely degraded in challenging urban environments due to NLOS receptions and multi-path interference. With the rapid development of urbanisation, the emergence of new construction materials as well as the complex architectures of modern buildings, the physic-based GNSS signal propagation channel modeling has encountered the bottleneck especially for the local effects. That's why the data-driven approach is promising to mitigate GNSS NLOS in a robust way. This paper first analyzes the main limitations of the current existing AI-based GNSS LOS/NLOS classifiers. Accordingly, enriched features are proposed and selected by correlation and predictive power analysis associated with physical interpretations. Finally, eight Machine Learning-based LOS/NLOS classification models are trained and evaluated using the proposed feature on a huge real dataset of around 8 hours collected by a vehicle in different urban environments. The performances show that the Gradient Boosting model has the most balanced performance in terms of prediction accuracy (81.63%) and the prediction time, which is promising for real time implementation.