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

doi:10.1631/FITEE.1900523

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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

常规文章 | Updated：2022-05-19

- 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
- A convolutional neural network based approach to sea clutter suppression for small boat detection
- 信息与电子工程前沿（英文） 2020年21卷第10期页码：1504-1520
- Affiliations：
  
  National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  [ "Guan-qing LI, E-mail: liguanqing09@nudt.edu.cn" ]
  Zhi-yong SONG, E-mail:songzhiyong08@nudt.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900523
  中图分类号： TN957.51
- 收稿：2019-09-25，
  
  修回：2020-;5-18，
  
  纸质出版：2020-10
- Accepted：
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李官清, 宋志勇, 付强. 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[J]. 信息与电子工程前沿（英文）, 2020,21(10):1504-1520.

LI Guan-qing, SONG Zhi-yong, FU Qiang. 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.
李官清, 宋志勇, 付强. 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[J]. 信息与电子工程前沿（英文）, 2020,21(10):1504-1520. DOI： 10.1631/FITEE.1900523.

LI Guan-qing, SONG Zhi-yong, FU Qiang. 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. DOI： 10.1631/FITEE.1900523.

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。

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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