Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data

Abstract

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

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Cited by in corpus (6)

• Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization
• Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
• VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification
• GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
• Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization
• GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System