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 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Gözcü, Baran
Asaei, Afsaneh
Cevher, Volkan
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  • Gozcu_CAMSAP_2013.pdf