RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet

作     者：Sarkar, Shamik Guo, Dongning Cabric, Danijela 

作者机构：Indraprastha Institute of Information Technology Electronics and Communications Engineering Department Delhi110020 India Northwestern University Electrical and Computer Engineering Department EvanstonIL60208 United States University of California at Los Angeles Electrical and Computer Engineering Department Los AngelesCA90095 United States 

出 版 物：《IEEE Transactions on Cognitive Communications and Networking》 (IEEE Trans. Cogn. Commun. Netw.)

年 卷 期：2024年

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Signal to noise ratio 

摘      要：In emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS), the ability to detect radar signals without assistance from the radar transmitter is of paramount importance. This paper presents RadYOLOLet, a novel supervised deep learning-based spectrum sensing approach designed to detect low-power radar signals in the presence of interference and estimate the radar signal parameters. RadYOLOLet employs two independently trained convolutional neural networks, RadYOLO and Wavelet-CNN. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet unless the signal-to-noise-and-interference ratio (SINR) is very low. In such cases, Wavelet-CNN is utilized to operate on the continuous wavelet transform of the captured signals, enhancing the detection outcome. The performance of RadYOLOLet is evaluated through various experiments using different types of interference signals. The results indicate that RadYOLOLet maintains accurate radar detection performance for SINRs of 16 dB or higher for all five types of radar signals considered, which surpasses the capabilities of other comparable methods. © 2024 IEEE.