The issue of small-angle maneuvering targets inverse synthetic aperture radar (ISAR) imaging has been successfully addressed by popular motion compensation algorithms. However, when the target's rotational velocity is sufficiently high during the dwell time of the radar, such compensation algorithms cannot obtain a high quality image. This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm. The keystone transform is used to coarsely compensate for the target 's rotational motion and translational motion, and the deep learning algorithm is used to achieve a super-resolution image. The uniformly distributed point target data are used as the data set of the training u-net network. In addition, this method does not require estimating the motion parameters of the target, which simplifies the algorithm steps. Finally, several experiments are performed to demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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基于深度学习和Keystone变换的ISAR机动目标成像算法

流行的运动补偿算法已成功解决了小角度机动目标逆合成孔径雷达（ISAR）成像的问题。但是，当目标的旋转速度在雷达的停留时间内足够高时，这种补偿算法将无法获得高质量的图像。提出了一种基于梯形失真变换和深度学习算法的ISAR成像算法。梯形失真变换用于粗略补偿目标的旋转运动和平移运动，深度学习算法用于获得超分辨率图像。均匀分布的点目标数据用作训练u-net网络的数据集。此外，此方法不需要估算目标的运动参数，这简化了算法步骤。最后，进行了几次实验以证明所提算法的有效性。