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• W3100303558 abstract "Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness." @default.
• W3100303558 created "2020-11-23" @default.
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• W3100303558 date "2021-09-01" @default.
• W3100303558 modified "2023-10-14" @default.
• W3100303558 title "Imaging Enhancement via CNN in MIMO Virtual Array-Based Radar" @default.
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• W3100303558 doi "https://doi.org/10.1109/tgrs.2020.3035064" @default.
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