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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Guan-qing Li


Zhi-yong Song


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


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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author="Guan-qing Li, Zhi-yong Song, Qiang Fu",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A convolutional neural network based approach to sea clutter suppression for small boat detection
%A Guan-qing Li
%A Zhi-yong Song
%A Qiang Fu
%J Frontiers of Information Technology & Electronic Engineering
%V 21
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900523

T1 - A convolutional neural network based approach to sea clutter suppression for small boat detection
A1 - Guan-qing Li
A1 - Zhi-yong Song
A1 - Qiang Fu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 10
SP - 1504
EP - 1520
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900523

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.





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


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