典型文献
Radar emitter multi-label recognition based on residual network
文献摘要：
In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier trans-form(STFT).The time-frequency distribution image is then denoised using a deep normalized con-volutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the charac-teristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.
文献关键词：
中图分类号：
作者姓名：
Yu Hong-hai;Yan Xiao-peng;Liu Shao-kun;Li Ping;Hao Xin-hong
作者机构：
Science and Technology on Electromechanical Dynamic Control Laboratory,School of Mechatronical Engineering,Beijing Institute of Technology,Beijing,100081,China;Beijing Institute of Telemetry Technology,Beijing,100081,China
文献出处：
引用格式：
[1]Yu Hong-hai;Yan Xiao-peng;Liu Shao-kun;Li Ping;Hao Xin-hong-.Radar emitter multi-label recognition based on residual network)[J].防务技术,2022(03):410-417
A类：
DNCNN
B类：
Radar,emitter,label,recognition,residual,network,In,low,ratio,environments,traditional,radar,RER,method,struggles,multiple,signals,parallel,This,paper,proposes,classification,modulation,types,can,quickly,perform,domain,aliasing,under,SNRs,First,we,frequency,analysis,received,extract,normalized,image,through,short,Fourier,trans,STFT,distribution,then,denoised,using,deep,volutional,neural,Secondly,model,established,learning,charac,teristics,dataset,achieve,purpose,training,Finally,recognized,classified,thus,completing,automatic,Simulation,results,show,that,proposed,classify,different
AB值：
0.427906
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