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- W1999867771 abstract "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 <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ℓ</i> <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2,0</sub> 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 <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>a priori</i> . 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." @default.
- W1999867771 created "2016-06-24" @default.
- W1999867771 creator A5004318874 @default.
- W1999867771 creator A5033583535 @default.
- W1999867771 date "2010-09-01" @default.
- W1999867771 modified "2023-10-09" @default.
- W1999867771 title "Direction-of-Arrival Estimation Using a Mixed $ell _{2,0}$ Norm Approximation" @default.
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- W1999867771 doi "https://doi.org/10.1109/tsp.2010.2050477" @default.
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