一种基于模糊滤波提高SAR自动目标识别平移不变性的方法

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

Abstract

Abstract:

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.

Key words: synthetic aperture radar (SAR), automatic target recognition, translation invariance, convolutional neural network

CLC Number:

TP3174

Zhengqiang GU, Yan ZHANG, Bingchen ZHANG. Improving translation invariance of SAR automatic target recognition based on blur filtering method[J]. Systems Engineering and Electronics, 2020, 42(11): 2488-2496.

Figures/Tables 13

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References 23

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最大容忍值T	算法
最大容忍值T	原始网络	池化模糊	数据增强	并用	全模糊1	全模糊2
D7	1	4	5	7	6	8
BTR60	5	6	6	10	19	19
ZIL131	4	4	8	14	13	13
T62	5	5	5	5	8	9
BRDM-2	7	5	3	5	8	19

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

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