Causality-constrained multiple shift sequential matrix diagonalisation for parahermitian matrices

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

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

12 Citations (Scopus)


This paper introduces a causality constrained sequential matrix diagonalisation (SMD) algorithm, which generates a causal paraunitary transformation that approximately diagonalises and spectrally majorises a parahermitian matrix, and can be used to determine a polynomial eigenvalue decomposition. This algorithm builds on a multiple shift technique which speeds up diagonalisation per iteration step based on a particular search space, which is constrained to permit a maximum number of causal time shifts. The results presented in this paper show the performance in comparison to existing algorithms, in particular an unconstrained multiple shift SMD algorithm, from which our proposed method derives.
Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Print)978-0-9928626-1-9
Publication statusPublished - Sept 2014
Event22nd European Signal Processing Conference - Lisbon Congress Centre, Lisbon, Portugal
Duration: 1 Sept 20145 Sept 2014
Conference number: 2014


Conference22nd European Signal Processing Conference
Abbreviated titleEUSIPCO


  • eigenvalues and eigenfunctions
  • iterative methods
  • matrix decomposition
  • causal paraunitary transformation
  • causal time shifts


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