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基于子空间和稀疏贝叶斯学习的低信噪比下波达角估计方法 被引量：3

Subspace and Sparse Bayesian Learning-Based DOA Estimation Method with Low Signal-to-Noise Ratio
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摘要 为研究低信噪比条件下阵列信号处理中的波达角(Direction of arrival,DOA)估计问题,分析了低信噪比条件下信号子空间和噪声子空间的特征值表现,探讨了随机观测对子空间特征值的影响。提出了在低信噪比条件下对接收信号先进行子空间分离,后进行随机观测的降维处理方法,并将稀疏贝叶斯学习应用到DOA中,降低了DOA估计的复杂度,同时保证估计的精度。仿真实验表明,本算法在低信噪比条件下性能良好,对非相干源和相干源均有良好的估计性能。 To study the direction of arrival （DOA） problem in array signal processing with low signal-to-noise ratio （SNR） scene, the eigenvalue performance of signal subspace and noise subspace eigenvalues is analyzed, and the influence of random observation on subspace eigen- values is investigated. Under the condition of low SNR, the method of subspace separate is firstly proposed, followed by random observation; And the sparse Bayesian leaning （SBL）- based reconstruction method is applied to DOA, which reduces the computational complexity of DOA estimation while keeping the reconstruction precision. Simulation results show that the proposed algorithm performs well under low SNR condition for signals with and without any coherence.
作者 文方青 张弓 刘苏 贲德
机构地区 南京航空航天大学电子信息工程学院 南京电子技术研究所
出处 《数据采集与处理》 CSCD 北大核心 2013年第4期460-465,共6页 Journal of Data Acquisition and Processing
基金 国家自然科学基金(61071163 61071164 61271327)资助项目 航空科学基金(2011ZC52034)资助项目 江苏高校优势学科建设工程资助项目
关键词 子空间分离 稀疏贝叶斯学习 波达角估计 低信噪比 subspace separation sparse Bayesian learning direction of arrival（DOA） estima- tion low signal-to-noise ratio（SNR）
分类号 TN95 [电子电信—信号与信息处理]
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参考文献15

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二级参考文献99

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共引文献54

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二级引证文献10

数据采集与处理
2013年 第4期

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