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An algorithm for computing the eigenvalue decomposition of a para-Hermitian polynomial matrix is described. This amounts to diagonalizing the polynomial matrix by means of a paraunitary "similarity" transformation. The algorithm makes use of "elementary paraunitary transformations" and constitutes a generalization of the classical Jacobi algorithm for conventional Hermitian matrix diagonalization. A proof of convergence is presented. The application to signal processing is highlighted in terms of strong decorrelation and multichannel data compaction. Some simulated results are presented to demonstrate the capability of the algorithm
Published in:
Signal Processing, IEEE Transactions on
(Volume:55
,
Issue:
5
)
Date of Publication: May 2007
- Page(s):
- 2158 - 2169
- ISSN :
- 1053-587X
- INSPEC Accession Number:
- 9427740
- Digital Object Identifier :
- 10.1109/TSP.2007.893222
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 23 April 2007
- Issue Date :
- May 2007
- Sponsored by :
- IEEE Signal Processing Society
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