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Abstract
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue decomposition (PEVD). As an extension of the ordinary EVD to polynomial matrices, the PEVD will generate paraunitary matrices that diagonalise a parahermitian matrix. This paper compares the decomposition accuracies of two fundamentally different methods capable of computing an approximate PEVD. The first of these  sequential matrix diagonalisation (SMD)  iteratively decomposes a parahermitian matrix, while the second DFTbased algorithm computes a pointwise in frequency decomposition. We demonstrate through the use of examples that both algorithms can achieve varying levels of decomposition accuracy, and provide results that indicate the type of broadband multichannel problems that are better suited to each algorithm. It is shown that iterative methods, which generate paraunitary eigenvectors, are suited for general applications with a low number of sensors, while a DFTbased approach is useful for fixed, finite order decompositions with a small number of lags.
Original language  English 

Number of pages  5 
Publication status  Published  10 Dec 2017 
Event  IEEE 7th International Workshop on Computational Advances in MultiSensor Adaptive Processing  Curacao, Netherlands Antilles Duration: 10 Dec 2017 → 13 Dec 2017 http://www.cs.huji.ac.il/conferences/CAMSAP17/ 
Conference
Conference  IEEE 7th International Workshop on Computational Advances in MultiSensor Adaptive Processing 

Abbreviated title  CAMSAP 
Country/Territory  Netherlands Antilles 
City  Curacao 
Period  10/12/17 → 13/12/17 
Internet address 
Keywords
 polynomial matrix eigenvalue decompositions
 PEVD
 sequential matrix diagonalisation
 SMD
 broadband multichannel problems
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 1 Finished

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