An EVD Algorithm for Para-Hermitian Polynomial Matrices

Author

McWhirter, John G. ; Baxter, Paul D. ; Cooper, Tom ; Redif, Soydan ; Foster, Joanne

Author_Institution

QinetiQ Ltd, Malvern

Volume

55

Issue

5

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

2158

Lastpage

2169

Abstract

An algorithm for computing the eigenvalue decomposition of a para-Hermitian polynomial matrix is described. This amounts to diagonalizing the polynomial matrix by means of a paraunitary "similarity" transformation. The algorithm makes use of "elementary paraunitary transformations" and constitutes a generalization of the classical Jacobi algorithm for conventional Hermitian matrix diagonalization. A proof of convergence is presented. The application to signal processing is highlighted in terms of strong decorrelation and multichannel data compaction. Some simulated results are presented to demonstrate the capability of the algorithm

Keywords

decorrelation; eigenvalues and eigenfunctions; polynomial matrices; signal processing; EVD algorithm; Jacobi algorithm; decorrelation; eigenvalue decomposition; elementary paraunitary transformations; multichannel data compaction; para-Hermitian polynomial matrices; paraunitary similarity transformation; signal processing; Array signal processing; Compaction; Digital signal processing; Eigenvalues and eigenfunctions; Jacobian matrices; MIMO; Matrix decomposition; Polynomials; Sensor arrays; Signal processing algorithms; Broadband sensor array; convolutive mixing; multichannel data compaction; paraunitary matrix; polynomial matrix eigenvalue decomposition; strong decorrelation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2007.893222

Filename

4156408