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The power method in conjunction with deflation provides an economical approach to compute an eigenvalue decomposition (EVD) of a low-rank Hermitian matrix, which typically appears as a covariance matrix in narrowband sensor array processing. In this paper, we extend this idea to the broadband case, where a polynomial para-Hermitian matrix needs to be diagonalised. For the low-rank case, we combine a polynomial equivalent of the power method with a deflation approach to subsequently extract eigenpairs. We present perturbation analysis and simulation results based on an ensemble of low-rank randomized para-Hermitian matrices. The proposed approach demonstrates higher accuracy, faster execution time, and lower implementation cost than state-of-the-art algorithms.
|Number of pages
|Published - 13 Dec 2023
|9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Los Suenos, Costa Rica
Duration: 10 Dec 2023 → 13 Dec 2023
|9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
|10/12/23 → 13/12/23
- power method
- para-hermitian matrices
- eigenvalue decomposition (EVD)
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1/07/18 → 31/03/24
- 1 Conference contribution book