- Title
- A robust algorithm for joint-sparse recovery
- Creator
- Hyder, Md Mashud; Mahata, Kaushik
- Relation
- IEEE Signal Processing Letters Vol. 16, Issue 12, p. 1091-1094
- Publisher Link
- http://dx.doi.org/10.1109/lsp.2009.2028107
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2009
- Description
- 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.
- Subject
- basis pursuit; compressive sampling; joint-sparse; multiple measurement vectors; sparse representation
- Identifier
- http://hdl.handle.net/1959.13/916255
- Identifier
- uon:7925
- Identifier
- ISSN:1070-9908
- Rights
- Copyright © 2009 IEEE. Reprinted from IEEE Signal Processing Letters Vol. 16, Issue 12, p. 1091-1094. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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