Projects per year
Abstract
The Multiple Shift Maximum Element Sequential Matrix Diagonalisation (MSME-SMD) algorithm is a powerful but costly method for performing approximate polynomial eigenvalue decomposition (PEVD) for space-time covariance-type matrices encountered in e.g. broadband array processing. This paper discusses a newly developed search method that restricts the order growth within the MSME-SMD algorithm. In addition to enhanced control of the polynomial degree of the
paraunitary and parahermitian factors in this decomposition, the new search method is also computationally less demanding as fewer elements are searched compared to the original while the excellent diagonalisation of MSME-SMD is maintained.
paraunitary and parahermitian factors in this decomposition, the new search method is also computationally less demanding as fewer elements are searched compared to the original while the excellent diagonalisation of MSME-SMD is maintained.
| Original language | English |
|---|---|
| Title of host publication | 2nd IET International Conference on Intelligent Signal Processing 2015 |
| Place of Publication | Stevenage |
| Pages | 1-5 |
| Number of pages | 5 |
| Publication status | Published - Dec 2015 |
| Event | 2nd IET International Conference on Intelligent Signal Processing - Kensington Close Hotel, London, United Kingdom Duration: 1 Dec 2015 → 2 Dec 2015 |
Conference
| Conference | 2nd IET International Conference on Intelligent Signal Processing |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 1/12/15 → 2/12/15 |
Keywords
- multiple shift maximum element
- sequential matrix diagonalisation
- polynomial matrix eigenvalue decomposition
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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
Corr, J., Thompson, K., Weiss, S., Proudler, I. K., & McWhirter, J. G. (2015). Reduced search space multiple shift maximum element sequential matrix diagonalisation algorithm. In 2nd IET International Conference on Intelligent Signal Processing 2015 (pp. 1-5).