The polynomial matrix EVD (PEVD) is an extension of the conventional eigenvalue
decomposition (EVD) to polynomial matrices. The purpose of this article is to provide a …

An analytic parahermitian matrix admits in almost all cases an eigenvalue decomposition
(EVD) with analytic eigenvalues and eigenvectors. We have previously defined a discrete …

For parahermitian polynomial matrices, which can be used, for example, to characterize
space-time covariance in broadband array processing, the conventional eigenvalue …

An analytic parahermitian matrix admits an eigenvalue decomposition (EVD) with analytic
eigenvalues and eigenvectors except in the case of multiplexed data. In this paper, we …

This paper addresses the extension of the factorization of a Hermitian matrix by an
eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at …

This paper presents initial progress on formulating minimum variance distortionless
response (MVDR) broadband beam-forming using a generalised sidelobe canceller (GSC) …

We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix.
Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost …

In this article, we address the problem of singular value decomposition of polynomial
matrices and eigenvalue decomposition of para-Hermitian matrices. Discrete Fourier …

Extracting analytic eigenvectors from parahermitian matrices relies on phase smoothing in
the discrete Fourier transform (DFT) domain as its most expensive algorithmic component …

The polynomial power method repeatedly multiplies a polynomial vector by a para-
Hermitian matrix containing spectrally majorised eigenvalue to estimate the dominant …