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
Dictionary-Based Contrastive Learning for GNSS Jamming Detection
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学）；University of Management and Technology (UMT)（管理与技术大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Engineering and Technology (UET)（工程与技术大学）
Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Positioning Systems Lab, University of Technology Nürnberg (UTN)（定位系统实验室，图恩大学）；Center for Artificial Intelligence, Technical University of Applied Sciences Würzburg-Schweinfurt（人工智能中心，应用技术大学魏玛-施魏尔堡）
Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2025
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander University (FAU) Erlangen-Nürnberg（弗里德里希·亚历山大大学）；University of Navarra（纳瓦拉大学）；Tampere University（坦佩雷大学）；University of Applied Sciences Erlangen-Nuremberg（埃尔朗根-纽伦堡应用科学大学）
JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（中国电子科技大学无线通信国家重点实验室）；University of Electronic Science and Technology of China（中国电子科技大学）；Shanghai Aerospace Electronic Technology Institute（上海航天电子技术研究所）；Shanghai Key Laboratory of Collaborative Computing in Spacial Heterogeneous Networks (CCSN)（上海空间异构网络协同计算重点实验室）；Anhui Science and Technology University（安徽科技大学）；University of Oxford（牛津大学）；Shanghai Xiaoyuan Innovation center（上海小元创新中心）
