ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform
Shi Hongyin, Yue Liu, Jianwen Guo, Liu Mingxin
Abstract
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.