Full Text:   <252>

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CLC number: TN957.51

On-line Access: 2020-10-14

Received: 2019-09-25

Revision Accepted: 2020-03-22

Crosschecked: 2020-05-18

Cited: 0

Clicked: 274

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Guan-qing Li

https://orcid.org/0000-0002-9789-8931

Zhi-yong Song

https://orcid.org/0000-0002-3833-0510

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.10 P.1504-1520

http://doi.org/10.1631/FITEE.1900523


A convolutional neural network based approach to sea clutter suppression for small boat detection


Author(s):  Guan-qing Li, Zhi-yong Song, Qiang Fu

Affiliation(s):  National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   liguanqing09@nudt.edu.cn, songzhiyong08@nudt.edu.cn

Key Words:  Convolutional neural networks, Class activation map, Short-time Fourier transform, Small target detection, Sea clutter suppression


Guan-qing Li, Zhi-yong Song, Qiang Fu. A convolutional neural network based approach to sea clutter suppression for small boat detection[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1504-1520.

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pages="1504-1520",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900523"
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T1 - A convolutional neural network based approach to sea clutter suppression for small boat detection
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Abstract: 
Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios. In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm, to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface. Dual activation has two steps. First, we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer. Through this, we can obtain the class activation maps (CAMs), which correspond to the positive region of the sea clutter. Second, we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum. Then, we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps. In addition, we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy. Measurement on real datasets verified the effectiveness of the proposed method.

一种用于小船检测的基于卷积神经网络的海杂波抑制方法

李官清,宋志勇,付强
国防科技大学电子科学学院ATR国防科技重点实验室,中国长沙市,410073

摘要:目前的雷达目标检测方法通常基于高信杂比。本文提出一种新的基于卷积神经网络的双激活杂波抑制算法,以解决实际海况中低信杂比下的小目标检测问题。双激活有两个步骤。首先,激活最后一个全连接层的权重和来自上采样层的特征图获得类激活图,对应于海杂波的轮廓;其次,将类激活图反向映射到海杂波频谱得到抑制系数。抑制系数与原始距离多普勒图相乘即得到杂波抑制后的距离多普勒图。此外,提出一种基于采样的数据增强方法和一种有效的多类编码方法以提高预测精度。实测数据验证了方法的有效性。

关键词:卷积神经网络;类激活图;短时傅立叶变换;小目标检测;海杂波抑制

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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