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An Improved SVD Algorithm Based on Virtual Matrix

机译:基于虚拟矩阵的改进SVD算法

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摘要

In this paper, an improved singular value decomposition (SVD) algorithm for high-resolution direction of arrival (DOA) estimation is proposed, which is based on virtual matrix, the SVD-VM algorithm for short. The virtual matrix is employed as the preprocessor for the uniform linear array (ULA), and then the rotational matrix in ESPRIT is used to estimate the directions of the coherent sources. The simulation results show that the SVD-VM algorithm provides higher resolution and robustness performance for coherent signals estimation than conventional singular value decomposition.
机译:提出了一种基于虚拟矩阵(SVD-VM)的改进的奇异值分解(SVD)算法,用于高分辨率到达方向(DOA)估计。使用虚拟矩阵作为统一线性阵列(ULA)的预处理器,然后使用ESPRIT中的旋转矩阵估计相干源的方向。仿真结果表明,与传统的奇异值分解相比,SVD-VM算法为相干信号估计提供了更高的分辨率和鲁棒性。

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