%Aigaion2 BibTeX export from Idiap Publications %Wednesday 06 November 2024 02:30:37 AM @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} }