Non-line-of-sight GNSS Signal Classification for Urban Navigation Using Machine Learning

The reception of non-line-of-sight (NLOS) signals is a prevalent issue for Global Navigation Satellite System (GNSS) applications in urban environments. Such signals can significantly degrade the positioning and navigation accuracy for pedestrians and vehicles. While various methods, such as dual-polarization antennas and 3D building models, have been proposed to identify NLOS signals, they often require additional equipment or impose computational burdens, which limits their practicality. In this study, we introduce a machine learning (ML)-based classifier designed to detect NLOS signals based solely on quality indicators extracted from raw GNSS observations. We examined several input features, including carrier-to-noise density and elevation, and analyzed their relative importance. The effectiveness of our approach was validated using multi-GNSS data collected statically in the city of Hannover. To establish ground truth (i.e., a target) for training and testing the model, we used ray tracing in combination with a 3D building model of Hannover. The developed ML-based classifier achieved an accuracy of approximately 90% for NLOS signal classification. Furthermore, a vehicle-borne data set was used to test the utility of the ML-based signal classifier for kinematic positioning. The performance of the ML-aided positioning solution was compared against a solution without NLOS classification (raw solution) and with the ray-tracing-based classification results (reference solution). It was found that the ML-based solution demonstrated positioning precisions of 0.47 m, 0.55 m and 1.02 in the east, north and up components, respectively. This represents improvements of 64.6%, 33.4% and 36.6% over the raw solution. Additionally, we examined the performance of the ML-based classifier across various urban environments along the vehicle trajectory to gain deeper insights.