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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.

Original language | English |
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Title of host publication | 2015 49th Asilomar Conference on Signals, Systems and Computers |

Place of Publication | Piscataway, N.J. |

Publisher | IEEE |

Pages | 1714-1718 |

Number of pages | 5 |

ISBN (Print) | 978-1-4673-8576-3 |

DOIs | |

Publication status | Published - 29 Feb 2016 |

Event | 49th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States Duration: 8 Nov 2015 → 11 Nov 2015 |

### Conference

Conference | 49th Asilomar Conference on Signals, Systems and Computers |
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Country | United States |

City | Pacific Grove |

Period | 8/11/15 → 11/11/15 |

### Keywords

- signal processing
- eigenvalues
- eigenfunctions
- Hermitian matrices

## Fingerprint Dive into the research topics of 'Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition'. Together they form a unique fingerprint.

## Projects

- 1 Finished

## Signal Processing Solutions for the Networked Battlespace

EPSRC (Engineering and Physical Sciences Research Council)

1/04/13 → 31/03/18

Project: Research

## 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