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.
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 language | English |
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Number of pages | 5 |
Publication status | Published - 27 Sept 2024 |
Event | IEEE High Performance Extreme Computing Conference - Waltham, MA, United States Duration: 23 Sept 2024 → 27 Sept 2024 https://ieee-hpec.org/ |
Conference
Conference | IEEE High Performance Extreme Computing Conference |
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Abbreviated title | HPEC'24 |
Country/Territory | United States |
City | Waltham, MA |
Period | 23/09/24 → 27/09/24 |
Internet address |
Keywords
- sensor array applications
- signal subspace
- space-time covariance matrices