Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. Inspired by recent work towards a low complexity divide-and-conquer PEVD algorithm, this paper analyses the performance of this algorithm - named divide-and-conquer sequential matrix diagonalisation (DC-SMD) - for applications involving broadband sensor arrays of various dimensionalities. We demonstrate that by using the DC-SMD algorithm instead of a traditional alternative, PEVD complexity and execution time can be significantly reduced. This reduction is shown to be especially impactful for broadband multichannel problems involving large arrays.

Conference

ConferenceIEEE International Workshop on Signal Processing Systems
Abbreviated titleSiPS 2017
CountryFrance
CityLorient
Period3/10/175/10/17

Fingerprint

Sensor arrays
Polynomials
Decomposition

Keywords

  • polynomial matrix eigenvalue decomposition
  • PEVD
  • sensor arrays
  • PEVD algorithms
  • signal processing

Cite this

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title = "Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays",
abstract = "A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. Inspired by recent work towards a low complexity divide-and-conquer PEVD algorithm, this paper analyses the performance of this algorithm - named divide-and-conquer sequential matrix diagonalisation (DC-SMD) - for applications involving broadband sensor arrays of various dimensionalities. We demonstrate that by using the DC-SMD algorithm instead of a traditional alternative, PEVD complexity and execution time can be significantly reduced. This reduction is shown to be especially impactful for broadband multichannel problems involving large arrays.",
keywords = "polynomial matrix eigenvalue decomposition, PEVD, sensor arrays, PEVD algorithms, signal processing",
author = "Coutts, {Fraser K.} and Keith Thompson and Stephan Weiss and Proudler, {Ian K.}",
year = "2017",
month = "10",
day = "3",
doi = "10.1109/SiPS.2017.8109976",
language = "English",
note = "IEEE International Workshop on Signal Processing Systems, SiPS 2017 ; Conference date: 03-10-2017 Through 05-10-2017",

}

Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays. / Coutts, Fraser K.; Thompson, Keith; Weiss, Stephan; Proudler, Ian K.

2017. Paper presented at IEEE International Workshop on Signal Processing Systems, Lorient, France.

Research output: Contribution to conferencePaper

TY - CONF

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AU - Thompson, Keith

AU - Weiss, Stephan

AU - Proudler, Ian K.

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AB - A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. Inspired by recent work towards a low complexity divide-and-conquer PEVD algorithm, this paper analyses the performance of this algorithm - named divide-and-conquer sequential matrix diagonalisation (DC-SMD) - for applications involving broadband sensor arrays of various dimensionalities. We demonstrate that by using the DC-SMD algorithm instead of a traditional alternative, PEVD complexity and execution time can be significantly reduced. This reduction is shown to be especially impactful for broadband multichannel problems involving large arrays.

KW - polynomial matrix eigenvalue decomposition

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KW - sensor arrays

KW - PEVD algorithms

KW - signal processing

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