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Abstract
A number of algorithms for the iterative calculation of 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. This paper addresses savings — both computationally and in terms of memory use — that exploit the parahermitian structure of the matrix being decomposed, and also suggests an implicit trimming approach to efficiently curb the polynomial order growth usually observed during iterations of the PEVD algorithms. We demonstrate that with the proposed techniques, both storage and computations can be significantly reduced, impacting on a number of broadband multichannel problems.
Original language | English |
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Title of host publication | 2016 24th European Signal Processing Conference |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 1633-1637 |
Number of pages | 5 |
ISBN (Print) | 9780992862657 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Event | 24th European Signal Processing Conference - Hilton Budapest, Budapest, Hungary Duration: 29 Aug 2016 → 2 Sep 2016 http://www.eusipco2016.org/ |
Publication series
Name | |
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ISSN (Electronic) | 2076-1465 |
Conference
Conference | 24th European Signal Processing Conference |
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Abbreviated title | EUSIPCO'16 |
Country/Territory | Hungary |
City | Budapest |
Period | 29/08/16 → 2/09/16 |
Internet address |
Keywords
- iterative calculation
- polynomial matrices
- polynomial matrix eigenvalue decomposition
- parahermitian matrix
- broadband multichannel problems
- narrowband
- diagonalization
- diagonalisation
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Dive into the research topics of 'Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms'. Together they form a unique fingerprint.Projects
- 1 Finished
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Signal Processing Solutions for the Networked Battlespace
Soraghan, J. & Weiss, S.
EPSRC (Engineering and Physical Sciences Research Council)
1/04/13 → 31/03/18
Project: Research