Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Zeliang Wang; John G.  McWhirter; Stephan Weiss

doi:10.1109/ACSSC.2015.7421442

Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Zeliang Wang, John G. McWhirter, Stephan Weiss

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

10 Citations (Scopus)

30 Downloads (Pure)

Abstract

In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm is based on an iterative method for polynomial matrix eigenvalue decomposition (PEVD). By using the PEVD algorithm, the multichannel spectral factorization problem is simply broken down to a set of single channel problems which can be solved by means of existing one-dimensional spectral factorization algorithms. In effect, it transforms the multichannel spectral factorization problem into one which is much easier to solve.

Original language	English
Title of host publication	2015 49th Asilomar Conference on Signals, Systems and Computers
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Pages	1714-1718
Number of pages	5
ISBN (Print)	978-1-4673-8576-3
DOIs	https://doi.org/10.1109/ACSSC.2015.7421442
Publication status	Published - 29 Feb 2016
Event	49th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States Duration: 8 Nov 2015 → 11 Nov 2015

Conference

Conference	49th Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove
Period	8/11/15 → 11/11/15

Keywords

signal processing
eigenvalues
eigenfunctions
Hermitian matrices

Access to Document

10.1109/ACSSC.2015.7421442

Wang-etal-Asilomar-2015-Multichannel-spectral-factorization-algorithm-using-polynomialAccepted author manuscript, 247 KB

Cite this

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title = "Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition",

abstract = "In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm is based on an iterative method for polynomial matrix eigenvalue decomposition (PEVD). By using the PEVD algorithm, the multichannel spectral factorization problem is simply broken down to a set of single channel problems which can be solved by means of existing one-dimensional spectral factorization algorithms. In effect, it transforms the multichannel spectral factorization problem into one which is much easier to solve.",

keywords = "signal processing, eigenvalues, eigenfunctions, Hermitian matrices",

author = "Zeliang Wang and McWhirter, {John G.} and Stephan Weiss",

note = "(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; 49th Asilomar Conference on Signals, Systems and Computers ; Conference date: 08-11-2015 Through 11-11-2015",

year = "2016",

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doi = "10.1109/ACSSC.2015.7421442",

language = "English",

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pages = "1714--1718",

booktitle = "2015 49th Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE",

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Wang, Z, McWhirter, JG & Weiss, S 2016, Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition. in 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, Piscataway, N.J., pp. 1714-1718, 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, United States, 8/11/15. https://doi.org/10.1109/ACSSC.2015.7421442

TY - GEN

T1 - Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

AU - Wang, Zeliang

AU - McWhirter, John G.

AU - Weiss, Stephan

N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2016/2/29

Y1 - 2016/2/29

N2 - In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm is based on an iterative method for polynomial matrix eigenvalue decomposition (PEVD). By using the PEVD algorithm, the multichannel spectral factorization problem is simply broken down to a set of single channel problems which can be solved by means of existing one-dimensional spectral factorization algorithms. In effect, it transforms the multichannel spectral factorization problem into one which is much easier to solve.

AB - In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm is based on an iterative method for polynomial matrix eigenvalue decomposition (PEVD). By using the PEVD algorithm, the multichannel spectral factorization problem is simply broken down to a set of single channel problems which can be solved by means of existing one-dimensional spectral factorization algorithms. In effect, it transforms the multichannel spectral factorization problem into one which is much easier to solve.

KW - signal processing

KW - eigenvalues

KW - eigenfunctions

KW - Hermitian matrices

UR - http://ieeexplore.ieee.org/document/7421442/

U2 - 10.1109/ACSSC.2015.7421442

DO - 10.1109/ACSSC.2015.7421442

M3 - Conference contribution book

SN - 978-1-4673-8576-3

SP - 1714

EP - 1718

BT - 2015 49th Asilomar Conference on Signals, Systems and Computers

PB - IEEE

CY - Piscataway, N.J.

T2 - 49th Asilomar Conference on Signals, Systems and Computers

Y2 - 8 November 2015 through 11 November 2015

ER -

Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

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Signal Processing Solutions for the Networked Battlespace

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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Abstract

Conference

Keywords

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Signal Processing Solutions for the Networked Battlespace

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