Manifold Sparse Beamforming
| Type of publication: | Conference paper |
| Citation: | Gozcu_CAMSAP_2013 |
| Publication status: | Published |
| Booktitle: | IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
| Year: | 2013 |
| Month: | December |
| Pages: | 113-116 |
| Publisher: | IEEE |
| Location: | Saint Martin, France |
| ISBN: | 978-1-4673-3144-9 |
| DOI: | 10.1109/CAMSAP.2013.6714020 |
| Abstract: | We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this setting, we show that the array weights for optimal signal estimation can admit a sparse representation on the array manifold. To exploit this structure, we propose a convex regularizer in a grid-free fashion, which requires semidefinite programming. We then provide numerical evidence showing that the new formulation can significantly outperform diagonal loading when the regularization parameters are correctly tuned. |
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| Projects |
Idiap IM2 |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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