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Direction-of-Arrival Estimation Using a Mixed \ell _{2,0} Norm Approximation

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2 Author(s)
Hyder, M.M. ; Dept. of Electr. Eng., Univ. of Newcastle, Callaghan, NSW, Australia ; Mahata, K.

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 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.

Published in:

Signal Processing, IEEE Transactions on  (Volume:58 ,  Issue: 9 )

Date of Publication:

Sept. 2010

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