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

翻译：干扰设备通过破坏全球导航卫星系统（GNSS）信号构成严重威胁，损害了精确定位的鲁棒性。在频率快照中检测异常对于有效对抗这些干扰至关重要。适应多样化、未见过的干扰特征的能力对于确保GNSS在现实应用中的可靠性至关重要。本文提出了一种小样本学习方法，用于适应新的干扰类别。我们的方法采用四元组选择，使模型能够利用各种正类和负类干扰学习表征。此外，我们的四元组变体基于偶然不确定性和认知不确定性选择配对，以区分相似类别。我们在一条高速公路上记录了包含八类干扰的数据集，在该数据集上，我们的四元组损失小样本方法在干扰分类准确率上以97.66%的表现优于其他小样本技术。数据集获取地址：https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway