Impact of source model matrix conditioning on iterative PEVD algorithms

J Corr; K Thompson; S Weiss; I K Proudler; J G McWhirter

doi:10.1049/cp.2015.1771

Impact of source model matrix conditioning on iterative PEVD algorithms

J Corr, K Thompson, S Weiss, I K Proudler, J G McWhirter

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

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Abstract

Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in broadband array problems. To factorise such matrices, a number of polynomial EVD (PEVD) algorithms have been suggested. At every step, these algorithms move various amounts of off-diagonal energy onto the diagonal, to eventually reach an approximate diagonalisation. In practical experiments, we have found that the relative performance of these algorithms depends quite significantly on the type of parahermitian matrix that is to be factorised. This paper aims to explore this performance space, and to provide some insight into the characteristics of PEVD algorithms.

Original language	English
DOIs	https://doi.org/10.1049/cp.2015.1771
Publication status	Published - 1 Dec 2015
Event	2nd IET International Conference on Intelligent Signal Processing - Kensington Close Hotel, London, United Kingdom Duration: 1 Dec 2015 → 2 Dec 2015

Conference

Conference	2nd IET International Conference on Intelligent Signal Processing
Country/Territory	United Kingdom
City	London
Period	1/12/15 → 2/12/15

Keywords

polynomial eigenvalue decomposition
broadband array processing
space-time covariance matrix
polynomial matrix
matrix factorisation
eigenvalues and eigenfunctions
PEVD algorithms

Access to Document

10.1049/cp.2015.1771

Corr-etal-ICISP-2015-Impact-of-source-model-matrix-conditioning-on-iterative-PEVD-algorithmsAccepted author manuscript, 334 KB

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

Cite this

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title = "Impact of source model matrix conditioning on iterative PEVD algorithms",

abstract = "Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in broadband array problems. To factorise such matrices, a number of polynomial EVD (PEVD) algorithms have been suggested. At every step, these algorithms move various amounts of off-diagonal energy onto the diagonal, to eventually reach an approximate diagonalisation. In practical experiments, we have found that the relative performance of these algorithms depends quite significantly on the type of parahermitian matrix that is to be factorised. This paper aims to explore this performance space, and to provide some insight into the characteristics of PEVD algorithms.",

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note = "This paper is a postprint of a paper submitted to and accepted for publication in 2nd IET International Conference on Intelligent Signal Processing and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library; 2nd IET International Conference on Intelligent Signal Processing ; Conference date: 01-12-2015 Through 02-12-2015",

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AU - Corr, J

AU - Thompson, K

AU - Weiss, S

AU - Proudler, I K

AU - McWhirter, J G

N1 - This paper is a postprint of a paper submitted to and accepted for publication in 2nd IET International Conference on Intelligent Signal Processing and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

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N2 - Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in broadband array problems. To factorise such matrices, a number of polynomial EVD (PEVD) algorithms have been suggested. At every step, these algorithms move various amounts of off-diagonal energy onto the diagonal, to eventually reach an approximate diagonalisation. In practical experiments, we have found that the relative performance of these algorithms depends quite significantly on the type of parahermitian matrix that is to be factorised. This paper aims to explore this performance space, and to provide some insight into the characteristics of PEVD algorithms.

AB - Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in broadband array problems. To factorise such matrices, a number of polynomial EVD (PEVD) algorithms have been suggested. At every step, these algorithms move various amounts of off-diagonal energy onto the diagonal, to eventually reach an approximate diagonalisation. In practical experiments, we have found that the relative performance of these algorithms depends quite significantly on the type of parahermitian matrix that is to be factorised. This paper aims to explore this performance space, and to provide some insight into the characteristics of PEVD algorithms.

KW - polynomial eigenvalue decomposition

KW - broadband array processing

KW - space-time covariance matrix

KW - polynomial matrix

KW - matrix factorisation

KW - eigenvalues and eigenfunctions

KW - PEVD algorithms

U2 - 10.1049/cp.2015.1771

DO - 10.1049/cp.2015.1771

M3 - Paper

T2 - 2nd IET International Conference on Intelligent Signal Processing

Y2 - 1 December 2015 through 2 December 2015

ER -

Impact of source model matrix conditioning on iterative PEVD algorithms

Abstract

Conference

Keywords

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Signal Processing Solutions for the Networked Battlespace

Cite this