Relevance of polynomial matrix decompositions to broadband blind signal separation
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)
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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: https://orcid.org/0000-0002-3486-7206 and McWhirter, John G.;-
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Item type: Article ID code: 59776 Dates: DateEvent31 May 2017Published25 November 2016Published Online24 November 2016AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 13 Feb 2017 11:35 Last modified: 18 Nov 2024 21:18 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/59776