Recognition Algorithm of Emitter Signals Based on PCA+CNN

Wenqiang Ye, Cong Peng
{"title":"Recognition Algorithm of Emitter Signals Based on PCA+CNN","authors":"Wenqiang Ye, Cong Peng","doi":"10.1109/IAEAC.2018.8577538","DOIUrl":null,"url":null,"abstract":"In order to solve the problem of low recognition rate of emitter signal under low SNR by the traditional method, a recognition algorithm based on PCA+CNN is proposed. The radar emitter signal is processed time-frequency image. The image is processed, and is reduced dimensionality by PCA. Learning model is adjusted by pretraining, and the softmax classifier commonly used on the pretraining model adopts supervised sizing and recognition, finally complete the identification task. The simulation results show that the algorithm can achieve high recognition rate, compared with traditional algorithm.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"36 1","pages":"2410-2414"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2018.8577538","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

In order to solve the problem of low recognition rate of emitter signal under low SNR by the traditional method, a recognition algorithm based on PCA+CNN is proposed. The radar emitter signal is processed time-frequency image. The image is processed, and is reduced dimensionality by PCA. Learning model is adjusted by pretraining, and the softmax classifier commonly used on the pretraining model adopts supervised sizing and recognition, finally complete the identification task. The simulation results show that the algorithm can achieve high recognition rate, compared with traditional algorithm.
基于PCA+CNN的发射信号识别算法
为了解决传统方法在低信噪比条件下对发射器信号识别率低的问题,提出了一种基于PCA+CNN的识别算法。雷达辐射源信号经时频图像处理。对图像进行处理,利用主成分分析法对图像进行降维。通过预训练调整学习模型,预训练模型上常用的softmax分类器采用有监督的分级和识别,最终完成识别任务。仿真结果表明,与传统算法相比,该算法具有较高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信