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Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

机译：基于稀疏贝叶斯学习的基于实值数组协方差的改进DOA估计

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摘要

To further improve the efficiency of sparse Bayesian learning (SBL) for direction of arrival (DOA) estimation, a real-valued (unitary) formulation of covariance vector-based relevance vector machine (CV-RVM) technique is proposed in this paper. The covariance matrix of the sensor output is firstly transformed into a real-valued covariance matrix via unitary transformation, and the real-valued covariance matrix can be sparsely represented in a real-valued over-complete dictionary. Then the sparse Bayesian learning technique implemented in real domain is used to estimate the DOA. According to the property of the real-valued covariance matrix, unitary single measurement vector (USMV) CV-RVM for uncorrelated signals and unitary multiple measurement vector (UMMV) CV-RVM for correlated signals are developed, respectively. Due to the fact that the proposed methods are implemented in real domain and the snapshots are doubled via unitary transformation, the proposed methods have lower computational cost and better performance compared to the original SMV CV-RVM and MMV CV-RVM. Simulation results show the effectiveness of the proposed methods.
机译：为了进一步提高稀疏贝叶斯学习（SBL）的到达方向（DOA）估计的效率，本文提出了一种基于协方差矢量的相关矢量机（CV-RVM）技术的实值（unit）公式。首先通过unit变换将传感器输出的协方差矩阵转换为实值协方差矩阵，然后可以在实值超完备字典中稀疏表示实值协方差矩阵。然后使用在实域中实现的稀疏贝叶斯学习技术来估计DOA。根据实值协方差矩阵的性质，分别开发了不相关信号的单一单测量矢量（USMV）CV-RVM和相关信号的单一多测量矢量（UMMV）CV-RVM。由于所提出的方法是在实域中实现的，并且快照通过单一变换而翻倍，因此与原始SMV CV-RVM和MMV CV-RVM相比，所提出的方法具有较低的计算成本和更好的性能。仿真结果表明了所提方法的有效性。

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  • 来源
    《Signal processing》 |2016年第12期|183-189|共7页
  • 作者

    Yi Wang; Minglei Yang; Baixiao Chen; Zhe Xiang;

  • 作者单位

    National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi 710071, China,Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, China;

    National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi 710071, China,Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, China;

    National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi 710071, China,Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, China;

    National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi 710071, China,Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, China;

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  • 正文语种 eng
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  • 关键词

    Direction of arrival; Unitary transformation; Sparse recovery; Sparse Bayesian learning;

    机译：到达方向;ary变换恢复稀疏;稀疏贝叶斯学习;

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