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Detection of radar signals without assistance from the radar transmitter is acrucial requirement for emerging and future shared-spectrum wireless networkslike Citizens Broadband Radio Service (CBRS). In this paper, we propose asupervised deep learning-based spectrum sensing approach called RadYOLOLet thatcan detect low-power radar signals in the presence of interference and estimatethe radar signal parameters. The core of RadYOLOLet is two differentconvolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trainedindependently. RadYOLO operates on spectrograms and provides most of thecapabilities of RadYOLOLet. However, it suffers from low radar detectionaccuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNNspecifically to deal with this limitation of RadYOLO. Wavelet-CNN operates oncontinuous Wavelet transform of the captured signals, and we use it only whenRadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLetusing different experiments corresponding to different types of interferencesignals. Based on our evaluations, we find that RadYOLOLet can achieve 100%radar detection accuracy for our considered radar types up to 16 dB SNR, whichcannot be guaranteed by other comparable methods. RadYOLOLet can also functionaccurately under interference up to 16 dB SINR.

RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet