Machine-Learning-Based GNSS Signal Anomaly Classification Using COSMIC-2 POD Data

Abstract

Ionospheric scintillation and radio frequency interference (RFI) affect Global Navigation Satellite System (GNSS) signals, making their distinction important for ionospheric monitoring and interference detection. This study identifies GNSS signal anomalies associated with scintillation and RFI using COSMIC-2 1 Hz precise orbit determination (POD) observations. Using 60 s per-satellite windows, dual-frequency time-series inputs and 16-dimensional statistical features were extracted, and weak labels for Normal, Scintillation, and RFI were generated from the amplitude scintillation index S4 and the RFI index. An InceptionTimeLite-FiLM-DeepSets joint multi-satellite model, where FiLM denotes feature-wise linear modulation, was trained and tested on 2024 data and directly applied to the full-year 2025 dataset. On the 2024 test set, recall for Normal, Scintillation, and RFI was 98.9%, 76.1%, and 58.5%, respectively, with a Macro-F1 of 0.8227 and a Matthews correlation coefficient of 0.7120. Predicted Scintillation occurrence rates were higher at low magnetic latitudes and during the postsunset premidnight period, whereas predicted RFI occurrence rates were concentrated over North Africa, the Middle East, South Asia, and Southeast Asia. These results show that, within the weak-label framework, COSMIC-2 1 Hz POD observations can support GNSS signal anomaly classification and spatiotemporal distribution analysis, providing a complementary approach for long-term anomaly analysis.