陈小龙,,
关键,
牟效乾,
刘宁波
海军航空大学 ??烟台 ??264001
基金项目:国家自然科学基金（61871391，61501487，61871392，U1633122，61471382，61531020）;国防科技基金（2102024）;山东省高校科研发展计划（J17KB139）;泰山****和中国科协青年人才托举工程（YESS20160115）专项经费
详细信息
作者简介:苏宁远(1995–)，男，山东烟台人，硕士在读。主要研究方向为智能雷达信号处理、目标检测。E-mail: 965291799@qq.com
陈小龙(1985–)，男，山东烟台人，博士，讲师。研究领域包括雷达动目标检测、海杂波抑制、雷达信号精细化处理等。入选中国科协“青年人才托举工程”，获中国电子学会优秀博士学位论文奖，第十九届中国专利优秀奖，中国电子学会科技进步三等奖。E-mail: cxlcxl1209@163.com
关键：关 键(1968–)，男，辽宁锦州人，教授，博士生导师。主要研究方向为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
牟效乾(1995–)，男，山东烟台人，硕士在读。研究领域包括智能雷达信号处理、动目标检测等。E-mail: 1012226010@qq.com
刘宁波(1983–)，男，山东烟台人，博士，讲师，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
通讯作者:陈小龙? cxlcxl1209@163.com
计量
文章访问数:2998
HTML全文浏览量:800
PDF下载量:831
被引次数:0
出版历程
收稿日期:2018-09-14
修回日期:2018-10-16
Detection and Classification of Maritime Target with Micro-motion Based on CNNs
Su Ningyuan,Chen Xiaolong,,
Guan Jian,
Mou Xiaoqian,
Liu Ningbo
Naval Aviation University, Yantai 264001, China
Funds:The National Natural Science Foundation of China (61871391, 61501487, 61871392, U1633122, 61471382, 61531020), National Defense Science Foundation (2102024), Scientific Research Development of Shandong (J17KB139), Special Funds of Taishan Scholars of Shandong and Young Elite Scientist Sponsorship Program of CAST (YESS20160115)
摘要
摘要:该文利用深度学习的高维特征泛化学习能力，将卷积神经网络(CNN)用于海上目标微多普勒的检测和分类。首先，在海面微动目标模型的基础上，在实测海杂波背景中分别构建4种类型微动信号的2维时频图，并作为训练和测试数据集；然后，分别采用LeNet, AlexNet和GoogLeNet 3种CNN模型进行二元检测和多种微动类型分类，并进行比较，研究信杂比对检测和分类性能的影响。最后，与传统的支持向量机方法进行比较，结果表明，所提方法能够智能学习微动特征，具有更好的检测和分类性能，可为杂波背景下的雷达动目标检测和识别提供新的技术途径。
关键词:微多普勒/
雷达目标检测/
深度学习/
卷积神经网络(CNN)/
海杂波/
时频分析
Abstract:In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background.
Key words:Micro-Doppler/
Radar target detection/
Deep learning/
Convolutional Neural Network (CNN)/
Sea Clutter/
Time-frequency analysis
PDF全文下载地址:
https://plugin.sowise.cn/viewpdf/198_525_R18077
