Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
This work addresses the threat of GNSS jamming for navigation systems, offering an incremental improvement in classification accuracy.
The paper tackles the problem of detecting jamming interference in GNSS signals by proposing a few-shot learning approach with uncertainty-based quadruplet selection, achieving 97.66% accuracy in jammer classification on a new dataset.
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