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A Robust Algorithm for Joint-Sparse Recovery

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

We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed lscr2,0 norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.

Published in:

Signal Processing Letters, IEEE  (Volume:16 ,  Issue: 12 )

Date of Publication:

Dec. 2009

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