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

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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.
Original languageEnglish
Number of pages5
Publication statusPublished - 27 Sept 2024
EventIEEE High Performance Extreme Computing Conference - Waltham, MA, United States
Duration: 23 Sept 202427 Sept 2024
https://ieee-hpec.org/

Conference

ConferenceIEEE High Performance Extreme Computing Conference
Abbreviated titleHPEC'24
Country/TerritoryUnited States
CityWaltham, MA
Period23/09/2427/09/24
Internet address

Keywords

  • sensor array applications
  • signal subspace
  • space-time covariance matrices

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