Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS). In this paper, we propose a supervised deep learning-based spectrum sensing approach called RadYOLOLet that can detect low-power radar signals in the presence of interference and estimate the radar signal parameters. The core of RadYOLOLet is two different convolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trained independently. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet. However, it suffers from low radar detection accuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNN specifically to deal with this limitation of RadYOLO. Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLet using different experiments corresponding to different types of interference signals. Based on our evaluations, we find that RadYOLOLet can achieve 100% radar detection accuracy for our considered radar types up to 16 dB SNR, which cannot be guaranteed by other comparable methods. RadYOLOLet can also function accurately under interference up to 16 dB SINR.

翻译：在没有雷达发射机辅助的情况下检测雷达信号，是新兴及未来共享频谱无线网络（如公民宽带无线电服务CBRS）的关键需求。本文提出一种名为RadYOLOLet的基于监督深度学习的频谱感知方法，该方法可在存在干扰的情况下检测低功率雷达信号，并估计雷达信号参数。RadYOLOLet的核心是两个独立训练的卷积神经网络——RadYOLO与Wavelet-CNN。RadYOLO基于频谱图运行，提供了RadYOLOLet的大部分能力，但在低信噪比（SNR）条件下雷达检测精度较低。为此，我们专门开发了Wavelet-CNN以应对RadYOLO的局限性。Wavelet-CNN基于捕获信号的连续小波变换运行，且仅在RadYOLO未能检测到任何雷达信号时使用。我们通过对应不同类型干扰信号的多项实验全面评估了RadYOLOLet。评估结果表明，对于所考虑的雷达类型，RadYOLOLet在高达16 dB SNR下可实现100%的雷达检测精度，而其他可比方法无法保证这一点。此外，RadYOLOLet在高达16 dB SINR的干扰条件下仍能准确运行。