一种基于时频原子特征的雷达辐射源信号识别方法

提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频原子作为一组固定的特征;识别阶段,以原子和信号的内积的绝对值作为分类器的输入特征,采用有监督模糊自适应共振网络进行辐射源的自动识别.对5类典型雷达辐射源信号的实验结果表明,该方法大大减小了识别过程中特征提取的计算量,输入特征具有类内聚集性强、类间区分度大的特点,在信噪比大于3 dB时可以获得高的识别正确率....

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Published in红外与毫米波学报 Vol. 30; no. 6; pp. 566 - 570
Main Author 王希勤 刘婧瑶 孟华东
Format Journal Article
LanguageChinese
Published 清华大学电子工程系,北京,100084 2011
Subjects
时频原子
模糊自适应共振网络
特征提取
类区分度
雷达辐射源
雷达辐射源
特征提取
时频原子
类区分度
模糊自适应共振网络
Online AccessGet full text
ISSN1001-9014

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Abstract 提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频原子作为一组固定的特征;识别阶段,以原子和信号的内积的绝对值作为分类器的输入特征,采用有监督模糊自适应共振网络进行辐射源的自动识别.对5类典型雷达辐射源信号的实验结果表明,该方法大大减小了识别过程中特征提取的计算量,输入特征具有类内聚集性强、类间区分度大的特点,在信噪比大于3 dB时可以获得高的识别正确率.
AbstractList TN957%TN971.1; 提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频原子作为一组固定的特征；识别阶段,以原子和信号的内积的绝对值作为分类器的输入特征,采用有监督模糊自适应共振网络进行辐射源的自动识别.对5类典型雷达辐射源信号的实验结果表明,该方法大大减小了识别过程中特征提取的计算量,输入特征具有类内聚集性强、类间区分度大的特点,在信噪比大于3 dB时可以获得高的识别正确率.
提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频原子作为一组固定的特征;识别阶段,以原子和信号的内积的绝对值作为分类器的输入特征,采用有监督模糊自适应共振网络进行辐射源的自动识别.对5类典型雷达辐射源信号的实验结果表明,该方法大大减小了识别过程中特征提取的计算量,输入特征具有类内聚集性强、类间区分度大的特点,在信噪比大于3 dB时可以获得高的识别正确率.
Author 王希勤 刘婧瑶 孟华东
AuthorAffiliation 清华大学电子工程系,北京100084
AuthorAffiliation_xml – name: 清华大学电子工程系,北京,100084
Author_FL LIU Jing-Yao
LIU Yi-Min
WANG Xi-Qin
MENG Hua-Dong
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DocumentTitleAlternate A method for radar emitter signal recognition based on time-frequency atom features
DocumentTitle_FL A method for radar emitter signal recognition based on time-frequency atom features
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Keywords 雷达辐射源
特征提取
时频原子
类区分度
模糊自适应共振网络
Language Chinese
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Notes A novel method for radar emitter signal recognition based on time-frequency atom feature is presented.During training,based on the over-complete time-frequency atom dictionary,a few atoms which can separate different kinds of signals best are extracted as a set of fixed feature according to the class separability.During testing,the module of inner product between atoms and signals is used as the input feature for the fuzzy ARTMAP classifier,and the radar emitter signals can be recognized automatically.Experimental results of five kinds of typical radar emitter signals show that this method reduces the computational amount of feature extraction during testing obviously,and the input features have strong concentration within classes and large separability between classes.Our method can achieve high recognition accuracy at the SNR larger than 3 dB.
WANG Xi-Qin,LIU Jing-Yao,MENG Hua-Dong,LIU Yi-Min（Department of Electronic Engineering,Tsinghua University,Beijing 100084,China）
31-1577/TN
radar emitters; feature extra
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PublicationTitle 红外与毫米波学报
PublicationTitleAlternate Journal of Infrared and Millimeter Waves
PublicationTitle_FL Journal of Infrared and Millimeter Waves
PublicationYear 2011
Publisher 清华大学电子工程系,北京,100084
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Snippet 提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频原子作为一组固...
TN957%TN971.1; 提出了一种全新的基于时频原子特征的雷达辐射源信号识别方法.训练阶段,在过完备时频原子库的基础上,以类区分度为度量,提取少数最能区分不同类别信号的时频...
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SubjectTerms 时频原子
模糊自适应共振网络
特征提取
类区分度
雷达辐射源
Title 一种基于时频原子特征的雷达辐射源信号识别方法
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