相干信源条件下的稀疏贝叶斯DOA估计  

Sparse Bayesian Learning Method for DOA Estimationunder Coherent Sources

作　　者：何文超 梁龙凯 弓馨 HE Wenchao;LIANG Longkai;GONG Xin(College of Science and Engineering,Changchun Humanities and Sciences College,Changchun 130117,China)

机构地区：[1]长春人文学院理工学院,长春130117

出　　处：《电讯技术》2021年第8期993-998,共6页Telecommunication Engineering

基　　金：吉教科合字〔2016〕第518号。

摘　　要：为了解决相干信源条件下的离格波达方向(Direction of Arrival,DOA)估计问题,在现阶段研究成果的基础上,将子空间平滑技术(Subspace Smoothing,SS)与离格稀疏贝叶斯算法(Off-grid Sparse Bayesian Interference,OGSBI)相结合,提出了SS-OGSBI算法。为了提高算法在小快拍低信噪比下的性能,与子空间拟合(Weighted Subspace Fitting,WSF)技术相结合,提出了SS-WSF-OGSBI算法。与稀疏贝叶斯算法对比,所提算法在均方根误差及估计成功率上均具有明显优势。Direction of arrival(DOA)estimation of coherent sources is one of the important researches in the field of signal processing.In order to solve the problem of off-grid DOA estimation under the condition of coherent source,subspace smoothing technique(SS)is combined with off-grid sparse Bayesian interference(OGSBI)algorithm,and the SS-OGSBI is proposed.In order to improve the performance of the algorithm under low signal-to-noise ratio with fewer sample datas,the SS-WSF-OGSBI algorithm is proposed by combining with the weighted subspace fitting(WSF)technique.Compared with the current sparse Bayesian learning(SBL)algorithm,the proposed algorithm has obvious advantages in root mean square error and estimation success rate.

关 键 词：阵列信号处理 相干信源 DOA估计 加权子空间拟合 稀疏贝叶斯 

分 类 号：TN911.7[电子电信—通信与信息系统]