一种基于奇异值分解的解相干算法

季正燕; 陈辉; 张佳佳; 李帅; 陆晓飞

doi:10.11999/JEIT161157

一种基于奇异值分解的解相干算法

doi: 10.11999/JEIT161157

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出版历程
- 收稿日期: 2016-10-28
- 修回日期: 2017-04-21
- 刊出日期: 2017-08-19

Decorrelation Algorithm Based on Singular Value Decomposition

摘要

摘要: 该文针对稀疏重构解相干问题，利用接收数据厅奇导值分解(SVD)后的大特征值对应的特征矢量，提出一种改进解相干方法。该方法通过迭代这一特征矢量来重构角度，无需知道信号源的数目，即可准确重构角度信息，实现解相干。相对于经典SVD算法，所提算法运算速度更快，稀疏重构效果更优。理论分析和仿真结果都验证了算法的良好性能。
- 信号处理 /
- 稀疏重构 /
- 解相干 /
- 奇异值分解
Abstract: To solve the problem of coherent sources using sparse reconstruction method, this paper proposes an improved method for solving coherent sources using the eigenvectors corresponding to the largest eigenvalues after Singular Value Decomposition (SVD) decomposition of received data. The method reconstructs the angle by iterating the feature vector, and reconstructs the angle information accurately without knowing the number of the signal source. Compared with the classical SVD algorithm, the operation speed is faster, and the sparse reconstruction effect is better. Theoretical analysis and simulation results verify the good performance of the algorithm.
- Signal processing /
- Sparse reconstruction /
- Decoherence /
- Singular Value Decomposition (SVD)

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参考文献(18)

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