Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD

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Abstract

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.
Original languageEnglish
Pages8038-8042
Number of pages5
DOIs
Publication statusPublished - 16 May 2019
Event2019 International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Conference

Conference2019 International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • eigenvalues
  • parahermitian matrix
  • algorithm

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