评估机器学习在干扰分类与定位中的鲁棒性，覆盖多变环境挑战。
Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
- 构建低频天线采集的干扰数据集，含多径效应与多种干扰类型
- 129种视觉编码器模型测试显示，模型对带宽、功率变化具有强鲁棒性
- 通过不确定性分析验证模型泛化能力，适合真实场景部署
干扰设备会破坏全球导航卫星系统（GNSS）信号，严重威胁定位精度的可靠性。检测频谱快照中的异常是有效应对干扰的关键。首要措施包括准确分类干扰、表征并定位干扰源。本文提出一个大规模数据集，包含从低频天线获取的多种人为干扰快照，涵盖受控多径效应。目标是评估机器学习（ML）模型在多径变化、干扰类别、带宽、信号功率波动以及快照长度限制下的鲁棒性。同时，评估129种不同的视觉编码器模型在全部任务上的表现。通过分析随机不确定性和认知不确定性，证明模型在多样化场景中具备良好泛化能力，适用于实际应用。
原文摘要 · Abstract (English)
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary countermeasure involves the reliable classification of interferences and the characterization and localization of jamming devices. This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna that capture various generated interferences within a large-scale environment, including controlled multipath effects. Our objective is to assess the resilience of machine learning (ML) models against environmental changes, such as multipath effects, variations in interference attributes, such as interference class, bandwidth, and signal power, the accuracy of jamming device localization, and the constraints imposed by snapshot input lengths. Furthermore, we evaluate the performance of a diverse set of 129 distinct vision encoder models across all tasks. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptability of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. Dataset: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency
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