Relevance of polynomial matrix decompositions to broadband blind signal separation

Tools

Redif, Soydan and Weiss, Stephan and McWhirter, John G. (2017) Relevance of polynomial matrix decompositions to broadband blind signal separation. Signal Processing, 134. pp. 76-86. ISSN 0165-1684 (https://doi.org/10.1016/j.sigpro.2016.11.019)

[thumbnail of Redif-etal-SP-2017-Relevance-of-polynomial-matrix-decompositions]
Preview
 Text. Filename: Redif_etal_SP_2017_Relevance_of_polynomial_matrix_decompositions.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

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 review of the theoretical foundations of the PEVD and to highlight practical applications in the area of broadband blind source separation (BSS). Based on basic definitions of polynomial matrix terminology such as parahermitian and paraunitary matrices, strong decorrelation and spectral majorization, the PEVD and its theoretical foundations will be briefly outlined. The paper then focuses on the applicability of the PEVD and broadband subspace techniques — enabled by the diagonalization and spectral majorization capabilities of PEVD algorithms—to define broadband BSS solutions that generalise well-known narrowband techniques based on the EVD. This is achieved through the analysis of new results from three exemplar broadband BSS applications — underwater acoustics, radar clutter suppression, and domain-weighted broadband beamforming — and their comparison with classical broadband methods.

ORCID iDs

Redif, Soydan, Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206 and McWhirter, John G.;
CORE (COnnecting REpositories)