GNSS 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.