Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
To address unreliable GNSS positioning in vehicular systems caused by real-world interference, this study systematically evaluates supervised (CNN, LSTM, Transformer) and pseudo-label-based unsupervised learning for interference signal classification, using large-scale real-world measurements from German highways and Austrian Alpine roads. Its key contributions include: (i) the first empirical validation of pseudo-labeling unsupervised learning in large-scale realistic vehicular GNSS scenarios; (ii) identification of cross-regional environmental discrepancies as a critical constraint on model generalization; and (iii) proposal of a synergistic adaptation framework integrating anomaly detection (Isolation Forest), domain adaptation (DANN), and time-frequency-domain data augmentation. Experimental results show a classification accuracy of 98.2%, with pseudo-labeling achieving 92% of supervised method performance; DANN improves cross-scenario F1-score by 37%, significantly mitigating data distribution shift.