Generalized polynomial power method

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Abstract

The polynomial power method repeatedly multiplies a polynomial vector by a para-Hermitian matrix containing spectrally majorised eigenvalue to estimate the dominant eigenvector corresponding the dominant eigenvalue. To limit the order of the resulting vector, truncation is performed in each iteration. This paper extends the polynomial power method from para-Hermitian matrices to a general polynomial matrix for determining its dominant left- and right-singular vectors and the corresponding singular value. The proposed extension assumes that the dominant singular is positive on the unit circle. The resulting algorithm is compared with a state-of-the-art PSVD algorithm and provides better accuracy with reduced computation time and lower approximation orders for the decomposition.
Original languageEnglish
Title of host publication2023 Sensor Signal Processing for Defence Conference (SSPD)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages5
ISBN (Electronic)9798350337327
DOIs
Publication statusPublished - 22 Sept 2023
Event12th International Conference on Sensor Signal Processing for Defence - Edinburgh, United Kingdom
Duration: 12 Sept 202313 Sept 2023
https://sspd.eng.ed.ac.uk/

Conference

Conference12th International Conference on Sensor Signal Processing for Defence
Abbreviated titleSSPD'23
Country/TerritoryUnited Kingdom
CityEdinburgh
Period12/09/2313/09/23
Internet address

Keywords

  • polynomial power method
  • polynomial matrices
  • broadband sensor arrays
  • polynomial eigenvalue decomposition (PEVD)

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