Abstract. The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional imaging method based on the prior knowledge of imaging, the method proposed in this paper directly trains the ionospheric implicit imaging model of spaceborne SAR without complicated iterative processes. First, the phase errors, which are caused by the dispersion and the scintillation in the range and azimuth directions, respectively, are analyzed. Second, an improved u-net structure that embeds the residual network between the encoder and decoder is briefly proposed. Finally, the range doppler algorithm is used to preprocess radar image as the input of the convolutional neural network (CNN) and is compare with the predicted output of the CNN. Experimental results prove the effectiveness of the proposed method in radar image focusing.

中文翻译：

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用于 P 波段星载合成孔径雷达穿过电离层成像的深度卷积神经网络

摘要。背景电离层的色散特性和电离层不规则性的随机波动是严重损害雷达图像质量的相位误差的重要来源。为了减轻P波段星载合成孔径雷达（SAR）图像的电离层畸变，本文提出了一种超分辨率深度学习方法。与传统基于成像先验知识的成像方法不同，本文提出的方法直接训练星载SAR电离层隐式成像模型，无需复杂的迭代过程。首先，分析分别由距离和方位方向的色散和闪烁引起的相位误差。第二，简要提出了一种改进的 u-net 结构，该结构在编码器和解码器之间嵌入了残差网络。最后，使用距离多普勒算法作为卷积神经网络（CNN）的输入对雷达图像进行预处理，并与CNN的预测输出进行比较。实验结果证明了该方法在雷达图像聚焦方面的有效性。