This paper studies the impact of estimation errors in the sample space-time covariance matrix on its parahermitian matrix eigenvalue decomposition. We provide theoretical bounds for the perturbation of the ground-truth eigenvalues and of the subspaces of their corresponding eigenvectors. We show that for the eigenvalues, the perturbation depends on the norm of the estimation error in the space-time covariance matrix, while the perturbation of eigenvector subspaces can additionally be influenced by the spectral distance of the eigenvalues. We confirm these theoretical results by simulations.
|Number of pages||5|
|Publication status||Published - 8 Jul 2018|
|Event||10th IEEE Workshop on Sensor Array and Multichannel Signal Processing - Sheffield, United Kingdom|
Duration: 8 Jul 2018 → 11 Jul 2018
|Conference||10th IEEE Workshop on Sensor Array and Multichannel Signal Processing|
|Abbreviated title||SAM 2018|
|Period||8/07/18 → 11/07/18|
- broadband array processing
- space-time convariance estimation
- parahermitian matrix
- eigenvalue decomposition
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Best Student Paper Award
Delaosa, Connor (Recipient), Coutts, Fraser Kenneth (Recipient), Pestana, Jennifer (Recipient) & Weiss, Stephan (Recipient), 2018
Prize: Prize (including medals and awards)File