Abstract
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.
| 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 |
Fingerprint
Keywords
- multiple shift maximum element
- sequential matrix diagonalisation
- polynomial matrix eigenvalue decomposition
Cite this
}
Reduced search space multiple shift maximum element sequential matrix diagonalisation algorithm. / Corr, J; Thompson, K; Weiss, S; Proudler, I K; McWhirter, J G.
2nd IET International Conference on Intelligent Signal Processing 2015. Stevenage, 2015. p. 1-5.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Reduced search space multiple shift maximum element sequential matrix diagonalisation algorithm
AU - Corr, J
AU - Thompson, K
AU - Weiss, S
AU - Proudler, I K
AU - McWhirter, J G
PY - 2015/12
Y1 - 2015/12
N2 - 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 theparaunitary 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.
AB - 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 theparaunitary 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.
KW - multiple shift maximum element
KW - sequential matrix diagonalisation
KW - polynomial matrix eigenvalue decomposition
M3 - Conference contribution book
SN - 9781785611360
SP - 1
EP - 5
BT - 2nd IET International Conference on Intelligent Signal Processing 2015
CY - Stevenage
ER -