Divide-and-conquer sequential matrix diagonalisation for parahermitian matrices

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

Research output: Contribution to conferencePaper

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Abstract

A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and will diagonalise a parahermitian matrix via paraunitary operations. Inspired by the existence of low complexity divide-and-conquer solutions to eigenproblems, this paper addresses a divide-and-conquer approach to the PEVD utilising the sequential matrix diagonalisation (SMD) algorithm. We demonstrate that with the proposed techniques, encapsulated in a novel algorithm titled divide-and-conquer sequential matrix diagonalisation (DC-SMD), algorithm complexity can be significantly reduced. This reduction impacts on a number of broadband multichannel problems, including those involving large arrays.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 6 Sep 2017
EventIEEE Sensor Signal Processing in Defence Conference - London, London, United Kingdom
Duration: 6 Dec 20177 Dec 2017
http://www.sspdconference.org

Conference

ConferenceIEEE Sensor Signal Processing in Defence Conference
Abbreviated titleSSPD'17
CountryUnited Kingdom
CityLondon
Period6/12/177/12/17
Internet address

Keywords

  • polynomial matrices
  • polynomial matrix eigenvalue decomposition
  • parahermitian matrices

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    Coutts, F. K., Corr, J., Thompson, K., Proudler, I. K., & Weiss, S. (Accepted/In press). Divide-and-conquer sequential matrix diagonalisation for parahermitian matrices. Paper presented at IEEE Sensor Signal Processing in Defence Conference, London, United Kingdom.