论文标题
基于深度学习辅助压缩感应的自我监督增强的雷达成像
Self-supervised Enhanced Radar Imaging Based on Deep-Learning-Assisted Compressed Sensing
论文作者
论文摘要
传统的雷达成像方法遭受低分辨率和噪声抑制不良的问题。我们提出了一种新的雷达成像方法，基于自我监督的深度学习辅助压缩传感（SS-DL-CS-NET）。原始雷达图像作为网的输入。训练网以学习原始雷达图像和高质量雷达图像之间的映射功能。但是，无法获得高质量的雷达图像。我们通过使用雷达图像的稀疏性来解决这个问题。原始的雷达图像和带有零值的图像作为NET的参考。我们的网不需要很多数据才能训练。实际雷达数据用于评估所提出方法的性能。实验结果证明了该方法的优越性
Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original radar image as the input of net. The net is trained to learn the mapping function between the original radar image and the high quality radar image. However, the high quality radar image cant be obtained. We solve this problem by used the sparsity of radar image. The original radar image and image with the zeros value as the reference of net. Ours net dont need a lot of data to train. Real radar data are used to evaluate the performance of the proposed method. The experimental results demonstrate the superiority of the proposed method
