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

arXiv:2402.09466v1 Announce Type: cross
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 …

abstract adapt arxiv classification cs.cv data devices diverse eess.iv eess.sp few-shot few-shot learning global interference navigation robustness satellite threat type uncertainty