Projects per year
In recent years, several algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and uses paraunitary operations to diagonalise a parahermitian matrix. This paper addresses potential computational savings that can be applied to existing cyclic-by-row approaches for the PEVD. These savings are found during the search and rotation stages, and do not significantly impact on algorithm accuracy. We demonstrate that with the proposed techniques, computations can be significantly reduced. The benefits of this are important for a number of broadband multichannel problems.
|Number of pages||5|
|Publication status||Accepted/In press - 19 Jul 2016|
|Event||50th Asilomar Conference on Signals, Systems and Computers - Asilomar Conference Ground, Pacific Grove, CA, United States|
Duration: 6 Nov 2016 → 9 Nov 2016
|Conference||50th Asilomar Conference on Signals, Systems and Computers|
|Abbreviated title||Asilomar 2016|
|City||Pacific Grove, CA|
|Period||6/11/16 → 9/11/16|
- polynomial matrix eigenvalue decomposition
- space-time covariance matrices
- algorithmic cost reductions
- sequential matrix diagonalisation
Coutts, F. K., Corr, J., Thompson, K., Weiss, S., Proudler, I. K., & McWhirter, J. G. (Accepted/In press). Complexity and search space reduction in cyclic-by-row PEVD algorithms. Paper presented at 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States.