The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with less training samples. Hence, the authors are interested in exploiting and modifying the U-net to enhance the P-band ISAR imaging. In this study, in light of the analysis of the effect of the ionospheric total electron content on the ground-based P-band radar echo signal, a novel ISAR imaging method is proposed for the ionospheric effect correction based on the modified U-net and polar formatting algorithm (PFA). The PFA is performed for the phase error coarse compensation. Then, the phase error fine compensation is exploited by the trained U-net. The proposed method can adapt the ionosphere disturbances and show high performance in imaging quality and computational efficiency. The simulation results show the effectiveness of the proposed method.

中文翻译：

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基于极性格式化算法和卷积神经网络的P波段ISAR成像中的电离层校正

电离层在P波段逆合成孔径雷达（ISAR）系统中引起严重的相位误差，这使得很难获得高质量的图像。近年来，卷积神经网络（CNN）在信号处理中备受关注，它可以自动提取特征以实现图像超分辨率重建。作为一种基于CNN的流行网络，U-net可以使用较少的训练样本进行工作。因此，作者对利用和修改U-net以增强P波段ISAR成像感兴趣。本研究针对电离层总电子含量对地基P波段雷达回波信号的影响进行分析，提出了一种新的ISAR成像方法，该方法基于改进的U-网络和电离层校正技术。极坐标格式化算法（PFA）。为相位误差粗补偿执行PFA。然后，由受过训练的U网利用相位误差精细补偿。所提出的方法能够适应电离层扰动，并在成像质量和计算效率方面表现出较高的性能。仿真结果表明了该方法的有效性。