SAR images are a high-resolution map of surface target areas and terrain in the range and the cross-range dimension. Usually, moving targets appear as defocused and spatially displaced objects superimposed on the SAR map. In this paper, a new moving target refocusing imaging method based on Range Doppler (RD) Algorithm and convolutional neural network is proposed. Firstly, Range Doppler (RD) Algorithm is used to preprocess the echo data to obtain the blurring SAR image as the input data of convolution neural network. Secondly, according to SAR imaging characteristics, the original U-net structure is improved to extract the structure information of input data and provide good target reconstruction. The trained network can focus moving targets in case of different azimuth velocities. Finally, the experimental results prove the effectiveness of the proposed method in radar image focusing.

SAR图像是范围和跨范围维度中的目标表面区域和地形的高分辨率地图。通常，移动目标显示为SAR地图上散焦和空间移位的对象。提出了一种基于距离多普勒算法和卷积神经网络的运动目标重聚焦成像方法。首先，利用距离多普勒算法对回波数据进行预处理，得到模糊的SAR图像作为卷积神经网络的输入数据。其次，根据SAR成像特性，对原有的U网结构进行了改进，以提取输入数据的结构信息，并提供良好的目标重构。经过训练的网络可以在不同方位角速度的情况下集中移动目标。最后，