Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

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 u...

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Bibliographic Details
Published in:Conference record - Asilomar Conference on Signals, Systems, & Computers pp. 1714 - 1718
Main Authors: Wang, Zeliang, McWhirter, John G., Weiss, Stephan
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.11.2015
Subjects:
Algorithms
Approximation
Approximation algorithms
Decomposition
Eigenvalues
Factorization
Finite impulse response filters
Indexes
Iterative algorithms
Jacobian matrices
Matrix decomposition
Multichannel
Newton method
Polynomials
Signal processing
Signal processing algorithms
Spectra
ISSN:1058-6393
Online Access:Get full text
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Summary: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.
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SourceType-Conference Papers & Proceedings-2
ISSN:1058-6393
DOI:10.1109/ACSSC.2015.7421442