利用CNN的海上目标探测背景分类方法

doi:10.3969/j.issn.0372-2112.2019.12.008

电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2505-2514.DOI: 10.3969/j.issn.0372-2112.2019.12.008

利用CNN的海上目标探测背景分类方法

徐雅楠, 刘宁波, 丁昊, 关键, 黄勇

海军航空大学信息融合研究所, 山东烟台 264001

收稿日期:2018-09-27 修回日期:2019-03-21 出版日期:2019-12-25
- 作者简介:
- 徐雅楠 女,1995年10月出生,山东威海人,2017年进入海军海空大学航空预警学院.现为在读硕士生.主要研究方向为海杂波中目标检测的深度学习方法.E-mail:yt_yananxu@163.com;刘宁波 男,1983年出生,山东烟台人.博士后在读,海军航空大学航空预警学院副教授,研究方向为雷达信号处理,海杂波抑制与目标智能检测.E-mail:lnb198300@163.com;丁昊男,1983年出生,山东烟台人.博士,讲师,主要研究方向为海杂波特性认知,雷达目标检测等.入选中国科协"青年人才托举工程".E-Mail:hao3431@tom.com;关键男,1968年出生,辽宁锦州人,教授,博士生导师.主要研究方向包括雷达目标检测与跟踪、侦察图像处理和信息融合.获国家科技进步二等奖1项、军队科技进步一等奖2项,山东省技术发明一等奖1项;"百千万人才工程"国家级人选,入选教育部新世纪优秀人才支持计划.E-mail:guanjian_68@163.com
- 基金资助:
- 国家自然科学基金 (No.61871392，No.61531020，No.61871391）; 中国博士后科学基金 (No.2017M620862）

Background Classification Method for Marine Target Detection Based on CNN

XU Ya-nan, LIU Ning-bo, DING Hao, GUAN Jian, HUANG Yong

Information Fusion Institute, Naval Aviation University, Yantai, Shandong 264001, China

Received:2018-09-27 Revised:2019-03-21 Online:2019-12-25 Published:2019-12-25
- Supported by:
- National Natural Science Foundation of China (No.61871392, No.61531020, No.61871391); China Postdoctoral Science Foundation (No.2017M620862)

摘要/Abstract

摘要： 该文主要研究基于卷积神经网络（Convolutional Neural Networks，CNN）的海上目标探测背景分类方法.以CNN中的经典网络LeNet为例，基于IPIX雷达实测数据集，进行控制变量的模型训练，对分类准确率、训练速度、一维信号的二维特征图变化等进行分析，基于实测数据集验证了利用CNN在一维雷达回波信号中进行海杂波与噪声分类的可行性，并同步分析了数据预处理、单个样本序列长度、网络结构参数等影响因素对分类准确率的影响，并针对典型探测场景分类进行了验证.结果表明，LeNet卷积神经网络在海上探测背景区分方面，具有很高的分类准确率，并且数据预处理方式、单个样本序列长度对结果影响显著，而网络结构参数有一定的调节区间，在此区间内调整，影响不显著，所提方法在顺/逆浪向、高/低海况条件下杂波分类与杂噪分类方面具有很高的准确率.

关键词: 卷积神经网络, 海杂波, 探测背景, 分类

Abstract: In this paper, the background classification method of marine target detection based on convolutional neural network (CNN) is mainly studied. Taking LeNet as an example, based on the IPIX measured data set, the model training through controlling variables is carried out. The feasibility of using CNN in the classification of sea clutter and noise in one dimensional radar echo signal is studied, and the influence of factors such as data preprocessing, single sample sequence length and network structure parameters on classification accuracy is analyzed synchronously, and verified for the typical detection scene classification. The application results of measured data show that the proposed method has high accuracy in clutter classification and noise classification under the conditions of forward/reverse direction and high/low sea conditions.

Key words: convolution neural network, sea clutter, detection background, classification

中图分类号:

TN957.51

徐雅楠, 刘宁波, 丁昊, 等. 利用CNN的海上目标探测背景分类方法[J]. 电子学报, 2019, 47(12): 2505-2514.

XU Ya-nan, LIU Ning-bo, DING Hao, et al. Background Classification Method for Marine Target Detection Based on CNN[J]. Acta Electronica Sinica, 2019, 47(12): 2505-2514.

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利用CNN的海上目标探测背景分类方法

Background Classification Method for Marine Target Detection Based on CNN

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