  Jamming devices pose a significant threat by disrupting signals from the
global navigation satellite system (GNSS), compromising the robustness of
accurate positioning. Detecting anomalies in frequency snapshots is crucial to
counteract these interferences effectively. The ability to adapt to diverse,
unseen interference characteristics is essential for ensuring the reliability
of GNSS in real-world applications. In this paper, we propose a few-shot
learning (FSL) approach to adapt to new interference classes. Our method
employs quadruplet selection for the model to learn representations using
various positive and negative interference classes. Furthermore, our quadruplet
variant selects pairs based on the aleatoric and epistemic uncertainty to
differentiate between similar classes. We recorded a dataset at a motorway with
eight interference classes on which our FSL method with quadruplet loss
outperforms other FSL techniques in jammer classification accuracy with 97.66%.
Dataset available at:
https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway
