Iterative Approximation of Analytic Eigenvalues of a Parahermitian Matrix EVD
Iterative Approximation of Analytic Eigenvalues of a Parahermitian Matrix EVD
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帕埃尔米特矩阵 EVD 解析特征值的迭代逼近
DOI：
10.1109/icassp.2019.8682407
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
J. Pestana
中科院分区：
文献类型：
--
作者：
Stephan Weiss;I. Proudler;Fraser K. Coutts;J. Pestana
我们提出了一种从类厄米矩阵中提取解析特征值的算法。内部迭代在离散傅里叶变换域中操作，通过由平滑准则驱动的最大似然序列检测来重新建立库之间的丢失关联。外部迭代继续，直到达到了所提取的特征值的近似的期望精度。将该方法与现有算法进行了比较。
We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.