Abstract / truncated to 115 words (read the full abstract)

In broadband array processing applications, an extension of the eigenvalue decomposition (EVD) to parahermitian Laurent polynomial matrices - named the polynomial matrix EVD (PEVD) - has proven to be a useful tool for the decomposition of space-time covariance matrices and their associated cross-spectral density matrices. Existing PEVD methods typically operate in the time domain and utilise iterative frameworks established by the second-order sequential best rotation (SBR2) or sequential matrix diagonalisation (SMD) algorithms. However, motivated by recent discoveries that establish the existence of an analytic PEVD - which is rarely recovered by SBR2 or SMD - alternative algorithms that better meet analyticity by operating in the discrete Fourier transform (DFT)-domain have received increasing attention. While offering ... toggle 32 keywords

polynomial matrix eigenvalue decomposition PEVD algorithms parahermitian paraunitary polynomial eigenvalue polynomial eigenvector cross-spectral density space-time covariance broadband array processing EVD SVD QR divide-and-conquer low cost efficient implementation parallel processing sequential matrix diagonalisation SMD second order sequential best rotation SBR2 laurent polynomial dimensionality reduction minimum-order solution analytic filter bank angle of arrival polynomial music


Coutts, Fraser Kenneth
University of Strathclyde
Publication Year
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June 27, 2019

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