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Abstract
In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any paraHermitian 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 onedimensional 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  17141718 
Number of pages  5 
ISBN (Print)  9781467385763 
DOIs  
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
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Dive into the research topics of 'Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition'. Together they form a unique fingerprint.Projects
 1 Finished

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