parehermite矩阵EVD解析特征值的迭代逼近

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-16 DOI:10.1109/ICASSP.2019.8682407

Stephan Weiss, I. Proudler, Fraser K. Coutts, J. Pestana

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引用次数: 15

摘要

提出了一种从parparhertian矩阵中提取解析特征值的算法。在离散傅里叶变换域中操作，内部迭代通过平滑准则驱动的最大似然序列检测重新建立箱之间丢失的关联。外部迭代继续进行，直到所提取的特征值的近似值达到所需的精度。将该方法与现有算法进行了比较。

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Iterative Approximation of Analytic Eigenvalues of a Parahermitian Matrix EVD

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.

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来源期刊

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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