Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms

Jamie Corr, Keith Thompson, Stephan Weiss, Ian K. Proudler, John G. McWhirter

Research output: Contribution to conferencePaper

11 Citations (Scopus)
90 Downloads (Pure)

Abstract

This paper extends the analysis of the recently introduced row-shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) family of polynomial eigenvalue decomposition (PEVD) algorithms. The row-shift corrected truncation method utilises the ambiguity in the paraunitary matrices to reduce their order. The results presented in this paper compare the effect a simple change in PEVD method can have on the performance of the paraunitary truncation. In the case of the SMD algorithm the benefits of the new approach are reduced compared to what has been seen before however there is still a reduction in both reconstruction error and paraunitary matrix order.
Original languageEnglish
Pages1-5
Number of pages5
DOIs
Publication statusPublished - 11 Sep 2015
Event5th Conference of the Sensor Signal Processing for Defence - Royal College of Physicians of Edinburgh, Edinburgh, United Kingdom
Duration: 9 Jul 201510 Jul 2015
http://www.sspd.eng.ed.ac.uk/conference-archive/2015

Conference

Conference5th Conference of the Sensor Signal Processing for Defence
Abbreviated titleSSPD 2015
CountryUnited Kingdom
CityEdinburgh
Period9/07/1510/07/15
Internet address

Keywords

  • sequential matrix diagonalisation algorithms
  • polynomial eigenvalue decomposition
  • broadband array processing

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