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Abstract
As an extension of the ordinary EVD to polynomial matrices, the polynomial matrix eigenvalue decomposition (PEVD) will generate paraunitary matrices that diagonalise a parahermitian matrix. Frequencybased PEVD algorithms have shown promise for the decomposition of problems of finite order, but require a priori knowledge of the length of the decomposition. This paper presents a novel iterative frequencybased PEVD algorithm which can compute an accurate decomposition without requiring this information. We demonstrate through the use of simulations that the algorithm can achieve superior performance over existing iterative PEVD methods.
Original language  English 

Title of host publication  2018 52nd Asilomar Conference on Signals, Systems, and Computers 
Place of Publication  Piscataway, NJ 
Publisher  IEEE 
Number of pages  5 
ISBN (Electronic)  9781538692189 
ISBN (Print)  9781538692196 
DOIs  
Publication status  Published  21 Feb 2019 
Event  52nd Asilomar Conference on Signals, Systems, and Computers  Pacific Grove, United States Duration: 28 Oct 2018 → 31 Oct 2018 
Conference
Conference  52nd Asilomar Conference on Signals, Systems, and Computers 

Country/Territory  United States 
City  Pacific Grove 
Period  28/10/18 → 31/10/18 
Keywords
 polynomial matrices
 polynomial matrix eigenvalue decomposition (PEVD)
 parahermitian matrix
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Dive into the research topics of 'An iterative DFTbased approach to the polynomial matrix eigenvalue decomposition'. Together they form a unique fingerprint.Projects
 1 Finished

Signal Processing Solutions for the Networked Battlespace
EPSRC (Engineering and Physical Sciences Research Council)
1/04/13 → 31/03/18
Project: Research