GNSS Interference Spectrum Highway Dataset 1

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. We recorded a dataset with our own sensor station at a German highway with eight interference classes and three non-interference classes. Our baseline methods achieve an accuracy of 97.66%. This dataset allows the development and evaluation of machine learning methods, such as domain adaptation, few-shot learning, and continual learning.

干扰设备通过干扰全球导航卫星系统（GNSS）的信号，严重威胁了精准定位的可靠性。对频谱快照中异常的检测对于有效对抗这些干扰至关重要。适应多样化的、未见过的干扰特征，对于确保GNSS在实际应用中的可靠性至关重要。我们使用自建的传感器站，在德国的高速公路上记录了一个包含八个干扰类别和三个非干扰类别的数据集。我们的基线方法实现了97.66%的准确率。此数据集可用于机器学习方法的开发与评估，例如领域自适应、少样本学习和持续学习。