典型文献
DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar
文献摘要:
This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolu-tion.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required fre-quency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target's DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low sig-nal-to-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.
文献关键词:
中图分类号:
作者姓名:
WEN Jinfang;YI Jianxin;WAN Xianrong;GONG Ziping;SHEN Ji
作者机构:
School of Electronic Information,Wuhan University,Wuhan 430072,China
文献出处:
引用格式:
[1]WEN Jinfang;YI Jianxin;WAN Xianrong;GONG Ziping;SHEN Ji-.DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar)[J].系统工程与电子技术(英文版),2022(05):1052-1063
A类:
B类:
DOA,estimation,multi,frequency,sparse,Bayesian,learning,passive,radar,This,paper,considers,develops,direction,arrival,improve,accuracy,developed,exploits,sparsity,targets,spatial,domain,Specifically,we,first,required,channel,data,acquire,through,series,preprocessing,such,clutter,suppression,coherent,integration,beamforming,constant,false,alarm,rate,CFAR,detection,Then,framework,estimated,by,jointly,extracting,via,evidence,maximization,Simulation,results,show,that,has,better,resolution,than,other,existing,algorithms,especially,under,scenarios,low,sig,nal,noise,SNR,small,snapshots,Furthermore,effectiveness,verified,field,experimental,FM
AB值:
0.584409
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