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

Fraser K. Coutts, Keith Thompson, Stephan Weiss, Ian K. Proudler

Research output: Contribution to conferencePaper

3 Citations (Scopus)
17 Downloads (Pure)

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.
Original languageEnglish
Number of pages6
DOIs
Publication statusPublished - 3 Oct 2017
EventIEEE International Workshop on Signal Processing Systems - Lorient, France
Duration: 3 Oct 20175 Oct 2017

Conference

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

Keywords

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

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    Coutts, F. K., Thompson, K., Weiss, S., & Proudler, I. K. (2017). Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays. Paper presented at IEEE International Workshop on Signal Processing Systems, Lorient, France. https://doi.org/10.1109/SiPS.2017.8109976