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%.

我们提出了一种少样本学习方法来适应新的干扰类别，通过使用各种正面和负面干扰类别进行模型表示的四重选择。此外，我们的四重选择方法根据随机性和认识性不确定性选择配对，以区分相似类别。在一个有八种干扰类别的高速公路数据集上，我们的少样本学习方法使用四重选择损失，在干扰源分类准确性方面表现优于其他少样本学习技术，达到 97.66%。