GNSS Interference Spectrum & Low-Cost Controlled Indoor Dataset

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. We fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor. By using late fusion the classification accuracy of the classes FreqHopper, Modulated, and Noise increases while lowering the uncertainty of Multitone, Noise, and Pulsed. The improved classification capabilities allow for more reliable results even in challenging scenarios.

干扰信号会降低并破坏全球导航卫星系统（GNSS）接收机的性能，从而影响其定位精度。因此，有必要对其进行检测、分类和定位，以确保GNSS的正常运行。最先进的检测和分类潜在干扰信号的技术采用监督式深度学习。我们仅从单个带宽受限的低成本传感器中融合两种模态，而非采用细粒度的高分辨率传感器和粗粒度低分辨率低成本传感器。通过采用后期融合技术，FreqHopper、Modulated和Noise类别的分类精度得到提升，同时降低了Multitone、Noise和Pulsed的不确定性。这种改进的分类能力使得在复杂场景下也能获得更加可靠的结果。