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@INPROCEEDINGS{Gozcu_CAMSAP_2013,
                      author = {G{\"{o}}zc{\"{u}}, Baran and Asaei, Afsaneh and Cevher, Volkan},
                    projects = {Idiap, IM2},
                       month = dec,
                       title = {Manifold Sparse Beamforming},
                   booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing},
                        year = {2013},
                       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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2014/Gozcu_CAMSAP_2013.pdf}
}