ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2020,（6）
• 所属期刊栏目: DEFENCE ELECTRONICS TECHNOLOGY
• 页码范围: 1178-1185
• 页数: 8页
• 语种: 英文
• DOI: 10.23919/JSEE.2020.000090

摘要

The issue of small-angle maneuvering targets inverse synthetic aperture radar (ISAR) imaging has been successfully addressed by popular motion compensation algorithms.How-ever,when the target's rotational velocity is sufficiently high dur-ing the dwell time of the radar,such compensation algo-rithms 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 net-work.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 demonst-rate the effectiveness of the proposed algorithm.