Photonics-based 3D radar imaging with CNN-assisted fast and noise-resistant image construction

被引:9
|
作者
Sun, Guanqun [1 ]
Zhang, Fangzheng [1 ]
Gao, Bindong [1 ]
Zhou, Yuewen [1 ]
Xiang, Yu [1 ]
Pan, Shilong [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Minist Educ, Key Lab Radar Imaging & Microwave Photon, Nanjing 210016, Peoples R China
基金
中国国家自然科学基金;
关键词
HIGH-RESOLUTION; SIGNAL GENERATION;
D O I
10.1364/OE.427889
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Photonics-based high-resolution 3D radar imaging is demonstrated in which a convolutional neural network (CNN)-assisted back projection (BP) imaging method is applied to implement fast and noise-resistant image construction. The proposed system uses a 2D radar array with each element being a broadband radar transceiver realized by microwave photonic frequency multiplication and mixing. The CNN-assisted BP image construction is achieved by mapping low-resolution images to high-resolution images with a pre-trained 3D CNN, which greatly reduces the computational complexity and enhances the imaging speed compared with basic BP image construction. Besides, using noise-free or low-noise ground truth images for training the CNN, the CNN-assisted BP imaging method can suppress the noises, which helps to generate high-quality images. In the experiment, 3D radar imaging with a K-band photonics-based radar having a bandwidth of 8 GHz is performed, in which the imaging speed is enhanced by a factor of similar to 55.3 using the CNN-assisted BP imaging method. By comparing the peak signal to noise ratios (PSNR) of the generated images, the noise-resistant capability of the CNN-assisted BP method is soundly verified. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
引用
收藏
页码:19352 / 19361
页数:10
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