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This paper introduces a causality constrained sequential matrix diagonalisation (SMD) algorithm, which generates a causal paraunitary transformation that approximately diagonalises and spectrally majorises a parahermitian matrix, and can be used to determine a polynomial eigenvalue decomposition. This algorithm builds on a multiple shift technique which speeds up diagonalisation per iteration step based on a particular search space, which is constrained to permit a maximum number of causal time shifts. The results presented in this paper show the performance in comparison to existing algorithms, in particular an unconstrained multiple shift SMD algorithm, from which our proposed method derives.
|Title of host publication||2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)|
|Number of pages||5|
|Publication status||Published - Sep 2014|
|Event||22nd European Signal Processing Conference - Lisbon Congress Centre, Lisbon, Portugal|
Duration: 1 Sep 2014 → 5 Sep 2014
Conference number: 2014
|Conference||22nd European Signal Processing Conference|
|Period||1/09/14 → 5/09/14|
- eigenvalues and eigenfunctions
- iterative methods
- matrix decomposition
- causal paraunitary transformation
- causal time shifts
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- 1 Finished
Soraghan, J. & Weiss, S.
1/04/13 → 31/03/18