Direction-of-Arrival Estimation Using a Mixed [ell] 2,0 Norm Approximation
A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to...
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Veröffentlicht in: | IEEE transactions on signal processing 2010-09, Vol.58 (9), p.4646-4655 |
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Sprache: | eng |
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Zusammenfassung: | A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to minimize a mixed [ell] 2,0 norm approximation to deal with the joint-sparse recovery problem. Our algorithm can resolve closely spaced and highly correlated sources using a small number of noisy snapshots. Furthermore, the number of sources need not be known a priori. In addition, our algorithm can handle more sources than other state-of-the-art algorithms. For the broadband DOA estimation problem, our algorithm allows relaxing the half-wavelength spacing restriction, which leads to a significant improvement in the resolution limit. |
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ISSN: | 1053-587X 1941-0476 |
DOI: | 10.1109/TSP.2010.2050477 |