Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

Zeliang Wang, John G. McWhirter, Stephan Weiss

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

8 Citations (Scopus)
9 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2015 49th Asilomar Conference on Signals, Systems and Computers
Place of PublicationPiscataway, N.J.
PublisherIEEE
Pages1714-1718
Number of pages5
ISBN (Print)978-1-4673-8576-3
DOIs
Publication statusPublished - 29 Feb 2016
Event49th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States
Duration: 8 Nov 201511 Nov 2015

Conference

Conference49th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove
Period8/11/1511/11/15

Keywords

  • signal processing
  • eigenvalues
  • eigenfunctions
  • Hermitian matrices

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    Cite this

    Wang, Z., McWhirter, J. G., & Weiss, S. (2016). Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition. In 2015 49th Asilomar Conference on Signals, Systems and Computers (pp. 1714-1718). Piscataway, N.J.: IEEE. https://doi.org/10.1109/ACSSC.2015.7421442