Support estimation of analytic eigenvectors of parahermitian matrices

Khattak, Faizan and Proudler, Ian K. and Weiss, Stephan (2022) Support estimation of analytic eigenvectors of parahermitian matrices. In: International Conference on Recent Advances in Electrical Engineering and Computer Sciences, 2022-10-18 - 2022-10-20, Pakistan Institute of Engineering and Applied Sciences.

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Extracting analytic eigenvectors from parahermitian matrices relies on phase smoothing in the discrete Fourier transform (DFT) domain as its most expensive algorithmic component. Some algorithms require an a priori estimate of the eigenvector support and therefore the DFT length, while others iteratively increase the DFT. Thus in this document, we aim to complement the former and to reduce the computational load of the latter by estimating the time-domain support of eigenvectors. The proposed approach is validated via an ensemble of eigenvectors of known support, which the estimated support accurately matches.


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