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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Wang, Zeliang, McWhirter, John and Weiss, Stephan 2015. Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition. Presented at: 49th Annual Asilomar Conference on Signals, Systems, and Computers, California, USA, 8-11 November 2015. 49th Annual Asilomar Conference on Signals, Systems, and Computers. CA, USA: IEEE, pp. 1714-1718. 10.1109/ACSSC.2015.7421442

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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 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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher: IEEE
ISBN: 978-1-4673-8574-9
Last Modified: 09 Jun 2020 01:26
URI: http://orca.cardiff.ac.uk/id/eprint/87415

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