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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}
}