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相干信源条件下的稀疏贝叶斯DOA估计
何文超,梁龙凯,弓馨
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(长春人文学院理工学院，长春130117)

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。

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

DOI：

基金项目:吉教科合字〔2016〕第518号

Sparse Bayesian learning method for DOA estimation under coherent sources

HE Wenchao,LIANG Longkai,GONG Xin

(College of Science and Engineering,Changchun Humanities and Sciences College,Changchun 130117,China)

Abstract:

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.

Key words: array signal processing coherent sources DOA estimation weighted subspace fitting sparse Bayes