GNSS Interference Spectrum Controlled Low-Frequency

Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices. We introduce an extensive dataset compromising snapshots obtained from a low-frequency antenna, capturing diverse generated interferences within a large-scale environment including controlled multipath effects. Our objective is to assess the resilience of ML models against environmental changes, such as multipath effects, variations in interference attributes, such as the interference class, bandwidth, and signal-to-noise ratio, the accuracy jamming device localization, and the constraints imposed by snapshot input lengths. We demonstrate the adaptness of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications.

干扰设备通过扰乱全球导航卫星系统（GNSS）的信号，严重威胁了精准定位的可靠性。在频谱快照中检测异常对于有效对抗这些干扰至关重要。一项关键的初步措施涉及对干扰的可靠分类，以及干扰设备的特征描述和定位。我们引入了一个包含来自低频天线的频谱快照的广泛数据集，这些快照捕捉到了在大规模环境中生成的各种干扰，包括受控的多径效应。我们的目标是评估机器学习模型对环境变化的适应性，如多径效应、干扰属性的变化（如干扰类型、频宽和信噪比）、干扰设备定位的准确性以及快照输入长度的限制。我们展示了我们的模型在跨越多个方面进行泛化时的适应性，从而确立了其在实际应用中的适用性。

搜集汇总

背景与挑战

背景概述

该数据集是一个关于GNSS信号干扰的低频频谱数据集，包含在受控室内大规模环境中记录的多种干扰类别，旨在评估机器学习模型在环境变化下的鲁棒性。数据集分为两部分，分别针对多径效应和信噪比变化进行了专门设计，适用于信号处理和机器学习领域的研究。