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We developed a hardware setup that captures short, wideband snapshots in both E1 and E6 GNSS bands. This setup is mounted to a bridge over a motorway. The setup records 20 ms raw IQ snapshots triggered from the energy with a sample rate of 62.5 MHz, an analog bandwidth of 50MHz and an 8 bit bit-width. At certain frequencies the GPS/Galileo or GLONASS signals can easily be seen as a slight increase in the spectrum. Note that experts manually analyzed the datastreams by thresholding CN/0 and AGC values. Manual labeling of these snapshots has resulted in 11 classes: classes 0 to 2 represent samples with no interferences, distinguished by variations in background intensity, while classes 3 to 10 contain different interferences. The challenge lies in adapting to positive class labels with only a limited number of samples available. We partition the dataset into a 64% training set, 16% validation set, and a 20% test set split (balanced over the classes).

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. Here, literature proposes ResNet18 and TS-Transformer as they provide the most accurate classification rates on quasi-realistic GNSS signals. However, employing these methods individually, they only focus on either spatial or temporal information and discard information during optimization, thereby degrading classification accuracy. This paper proposes a deep learning framework that considers both the spatial and temporal relationships between samples when fusing ResNet18 and TS-Transformers with a joint loss function to compensate for the weaknesses of both methods considered individually. Our real-world experiments show that our novel fusion pipeline with an adapted late fusion technique and uncertainty measure significantly outperforms the state-of-the-art classifiers by 6.7% on average, even in complicated realistic scenarios with multipath propagation and environmental dynamics. This works even well (F-β=2 score about 80.1%), when we fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor. By using late fusion the classification accuracy of the classes FreqHopper, Modulated, and Noise increases while lowering the uncertainty of Multitone, Noise, and Pulsed. The improved classification capabilities allow for more reliable results even in challenging scenarios.

Implementation of ANFIS using the pyTorch framework