Scalable approach for analytic polynomial subspace projection matrices for a space-time covariance matrix
Khattak, Faizan A. and Bakhit, Mohammed and Proudler, Ian K. and Weiss, Stephan (2024) Scalable approach for analytic polynomial subspace projection matrices for a space-time covariance matrix. In: IEEE High Performance Extreme Computing Conference, 2024-09-23 - 2024-09-27. (In Press)
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Abstract
In sensor array applications, it can be advantageous to project data onto a given signal subspace, for example, to improve the SNR or as part of direction finding algorithms. In the broadband case, a projection operator can be derived via polynomial matrices and, more specifically, from a space- time covariance matrix. Traditional methods perform a complete polynomial eigenvalue decomposition (PEVD) to achieve this, which can be computationally intensive. We propose a novel method to compute these subspace matrices directly, without the need for a full PEVD. Our approach is evaluated against existing methods using an ensemble of randomized para-Hermitian matrices, demonstrating significant improvements in both accuracy and computation time.
ORCID iDs
Khattak, Faizan A., Bakhit, Mohammed, Proudler, Ian K. and Weiss, Stephan ORCID: https://orcid.org/0000-0002-3486-7206;-
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Item type: Conference or Workshop Item(Paper) ID code: 90560 Dates: DateEvent6 September 2024Published6 September 2024AcceptedSubjects: 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 Sep 2024 11:15 Last modified: 01 Oct 2024 15:08 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90560