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dc.contributor.authorLiu, J.
dc.contributor.authorZhou, W.
dc.contributor.authorJuwono, Filbert Hilman
dc.date.accessioned2018-12-13T09:11:04Z
dc.date.available2018-12-13T09:11:04Z
dc.date.created2018-12-12T02:47:05Z
dc.date.issued2017
dc.identifier.citationLiu, J. and Zhou, W. and Juwono, F.H. 2017. Joint smoothed I0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar. Sensors. 17 (5): 1068.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/71700
dc.identifier.doi10.3390/s17051068
dc.description.abstract

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.

dc.publisherMDPI Publishing
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleJoint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar
dc.typeJournal Article
dcterms.source.volume17
dcterms.source.number5
dcterms.source.issn1424-8220
dcterms.source.titleSensors
curtin.departmentCurtin Malaysia
curtin.accessStatusOpen access


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