CLC number: TN957.51
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-05-18
Cited: 0
Clicked: 6737
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="A convolutional neural network based approach to sea clutter suppression for small boat detection",
author="Guan-qing Li, Zhi-yong Song, Qiang Fu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="10",
pages="1504-1520",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900523"
}
%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
%N 10
%P 1504-1520
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900523
TY - JOUR
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
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.
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