Scalable approach for analytic polynomial subspace projection matrices for a space-time covariance matrix

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Khattak, Faizan A. and Bakhit, Mohammed and Proudler, Ian K. and Weiss, Stephan; (2025) Scalable approach for analytic polynomial subspace projection matrices for a space-time covariance matrix. In: 2024 IEEE High Performance Extreme Computing Conference (HPEC). 2024 IEEE High Performance Extreme Computing Conference (HPEC) . IEEE, USA. ISBN 979-8-3503-8713-1 (https://doi.org/10.1109/HPEC62836.2024.10938503)

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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 logoORCID: https://orcid.org/0000-0002-3486-7206;
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