GNSS Interference Monitoring: Resilience of Machine Learning Methods on Public Real-World Datasets - Heublein, Lucas; Raichur, Nisha Lakshmana; Feigl, Tobias; Brieger, Tobias; Heuer, Fin; Asbach, Lennart; Ruegamer, Alexander; Ott, Felix (Navigation, Journal of the Institute of Navigation, 2024)
The accuracy of vehicle localization is critical for applications like self-driving cars, toll systems, and digital tachographs. Vehicles rely on GNSS receivers for positioning, but GNSS signals can be disrupted by interference, necessitating its identification, classification, purpose determination, and localization to mitigate effects. Machine learning (ML) methods have shown promise in interference monitoring, though their real-world applicability remains uncertain. These methods require realistic, labeled datasets that reflect operational noise and multipath effects, but creating such datasets is challenging due to legal restrictions on causing GNSS interference. To address this, we conducted large-scale measurement campaigns in real-world settings (highways in Germany and the Seetal Alps in Austria) and controlled indoor environments. We evaluated supervised ML methods, explored pseudo-labeling for unsupervised learning, and investigated challenges like dataset discrepancies, outlier detection, domain adaptation, and data augmentation. This study highlights the potential of ML models to adapt to varying datasets and reports on their performance in practical scenario
Keywords: Global navigation satellite system (GNSS); interference classification; machine learning; datasets; domain discrepancy; unsupervised learning; domain adaptation; outlier detection; multipath; toll collection