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 itera...

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Bibliographic Details
Main Authors: Weiss, Stephan, Proudler, Ian K., Coutts, Fraser K., Pestana, Jennifer
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrices
Discrete Fourier transforms
Eigenvalues and eigenfunctions
Matrices
Measurement
Signal processing algorithms
Time-domain analysis
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Description
Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682407