Manifold Sparse Beamforming

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.


Published in:
Proceedings of the 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 113-116
Presented at:
5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Saint Martin, France, December 15-18, 2013
Year:
2013
Keywords:
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 Record created 2013-10-28, last modified 2018-03-18

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