Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Wang, Zeliang and McWhirter, John G. and Weiss, Stephan; (2016) Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition. In: 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, USA, pp. 1714-1718. ISBN 978-1-4673-8576-3 (https://doi.org/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.

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