Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition

被引：0
作者：
He, Qishan [1 ]
Zhao, Lingjun [1 ]
Ji, Kefeng [1 ]
Kuang, Gangyao [1 ]
机构：
[1] Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 10期
关键词：
Automatic Target Recognition (ATR); Deep learning; Domain adaptation; Parameter sensitivity; Synthetic Aperture Radar (SAR);
D O I：
10.11999/JEIT240155
中图分类号：
学科分类号：
摘要：
With the development of artificial intelligence technology, Synthetic Aperture Radar (SAR) target recognition based on deep neural networks has received widespread attention. However, the imaging mechanism of SAR system leads to a strong correlation between image characteristics and imaging parameters, so the algorithm accuracy under deep learning is easily disturbed by the sensitivity of imaging parameters, which becomes a major obstacle restricting the deployment of advanced intelligent algorithms to practical engineering applications. Firstly, in this paper, the developments of SAR image target recognition technology and related data sets are reviewed, and the influence of imaging parameters on image characteristics is analyzed deeply from three aspects, i.e., imaging geometry, radar parameter and noise interference. Then, the existing literature on the robustness and generalization of deep learning technology to imaging parameter sensitivity is summarized from the three dimensions of model, data and features. Thereafter, the experimental results of typical methods are summarized and analyzed. Finally, the research direction of deep learning technology which is expected to break through the sensitivity of imaging parameters in the future is discussed. © 2024 Science Press. All rights reserved.
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页码：3827 / 3848
页数：21
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