Improving translation invariance of SAR automatic target recognition based on blur filtering method

被引：0

作者：

Gu Z. ^{[1
,2
,3
]}

Zhang Y. ^{[1
,2
,3
]}

Zhang B. ^{[1
,2
]}

机构：

[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

[2] Key Laboratory of Technology in Geospatial Information Processing and Applications System, Beijing

[3] University of Chinese Academy of Sciences, Beijing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Automatic target recognition; Convolutional neural network; Synthetic aperture radar (SAR); Translation invariance;

D O I：

10.3969/j.issn.1001-506X.2020.11.10

中图分类号：

学科分类号：

摘要：

Using convolutional neural network to realize synthetic aperture radar (SAR), auto target recognition (ATR) has become a hot spot in recent years. However, a hidden problem in practical use is the loss of translation invariance. As the target moves horizontally, vertically or both, the output of the system changes accordingly, which leads to misidentification. A practical solution is proposed at the model level. Through the improvement of the model algorithm, the translation invariance of the SAR ATR system is significantly improved, and there is no need for data augmentation. The proposed module is easy to be inserted into the existing SAR ATR backbone network, which is well compatible, has effect on recognition accuracy and achieves an approximate or slightly higher accuracy than the original network. Experiments demonstrate that the algorithm proposed not only improves the translation invariance of the system, but also improves the anti-interference ability of the system. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：2488 / 2496

页数：8

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