Improved eigenstructure-based 2D DOA estimation approaches based on nyström approximation
Improved eigenstructure-based 2D DOA estimation approaches based on nyström approximation
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DOI：
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发表时间：
2019-02
影响因子：
4.1
通讯作者：
Lingwen Zhang;Siliang Wu;Guanze Peng;Wenkao Yang
中科院分区：
文献类型：
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作者：
Lingwen Zhang;Siliang Wu;Guanze Peng;Wenkao Yang
本文提出了一种改进的均匀矩形阵二维波达方向估计方法。不同于传统的基于特征结构的估计方法，如多信号分类（MUSIC）和旋转不变技术的信号参数估计（ESPRIT），所提出的方法估计信号和噪声子空间与Nyström近似，该方法只需计算整个样本协方差矩阵的两个子矩阵，避免了直接计算特征值分解的需要样本协方差矩阵。因此，所提出的方法可以大大提高计算效率的大规模URA。数值结果验证了所提方法的可靠性和有效性。
In this paper, we propose improved approaches for two-dimensional (2D) direction-of-arrival (DOA) estimation for a uniform rectangular array (URA). Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), the proposed approaches estimate signal and noise subspaces with Nyström approximation, which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition (EVD) of the sample covariance matrix. Hence, the proposed approaches can improve the computational efficiency greatly for large-scale URAs. Numerical results verify the reliability and efficiency of the proposed approaches.