### Abstract

Language | English |
---|---|

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 |
---|---|

Country | United States |

City | Pacific Grove |

Period | 8/11/15 → 11/11/15 |

### Fingerprint

### Keywords

- signal processing
- eigenvalues
- eigenfunctions
- Hermitian matrices

### Cite this

*2015 49th Asilomar Conference on Signals, Systems and Computers*(pp. 1714-1718). Piscataway, N.J.: IEEE. https://doi.org/10.1109/ACSSC.2015.7421442

}

*2015 49th Asilomar Conference on Signals, Systems and Computers.*IEEE, Piscataway, N.J., pp. 1714-1718, 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, United States, 8/11/15. https://doi.org/10.1109/ACSSC.2015.7421442

**Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition.** / Wang, Zeliang; McWhirter, John G. ; Weiss, Stephan.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition

AU - Wang, Zeliang

AU - McWhirter, John G.

AU - Weiss, Stephan

N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2016/2/29

Y1 - 2016/2/29

N2 - 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.

AB - 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.

KW - signal processing

KW - eigenvalues

KW - eigenfunctions

KW - Hermitian matrices

UR - http://ieeexplore.ieee.org/document/7421442/

U2 - 10.1109/ACSSC.2015.7421442

DO - 10.1109/ACSSC.2015.7421442

M3 - Conference contribution book

SN - 978-1-4673-8576-3

SP - 1714

EP - 1718

BT - 2015 49th Asilomar Conference on Signals, Systems and Computers

PB - IEEE

CY - Piscataway, N.J.

ER -