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A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and will diagonalise a parahermitian matrix via paraunitary operations. Inspired by the existence of low complexity divide-and-conquer solutions to eigenproblems, this paper addresses a divide-and-conquer approach to the PEVD utilising the sequential matrix diagonalisation (SMD) algorithm. We demonstrate that with the proposed techniques, encapsulated in a novel algorithm titled divide-and-conquer sequential matrix diagonalisation (DC-SMD), algorithm complexity can be significantly reduced. This reduction impacts on a number of broadband multichannel problems, including those involving large arrays.
|Number of pages||5|
|Publication status||Accepted/In press - 6 Sep 2017|
|Event||IEEE Sensor Signal Processing in Defence Conference - London, London, United Kingdom|
Duration: 6 Dec 2017 → 7 Dec 2017
|Conference||IEEE Sensor Signal Processing in Defence Conference|
|Period||6/12/17 → 7/12/17|
- polynomial matrices
- polynomial matrix eigenvalue decomposition
- parahermitian matrices
Coutts, F. K., Corr, J., Thompson, K., Proudler, I. K., & Weiss, S. (Accepted/In press). Divide-and-conquer sequential matrix diagonalisation for parahermitian matrices. Paper presented at IEEE Sensor Signal Processing in Defence Conference, London, United Kingdom.