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dc.contributor.authorTeke O.en_US
dc.contributor.authorGurbuz, A.C.en_US
dc.contributor.authorArikan, O.en_US
dc.date.accessioned2016-02-08T12:21:52Z
dc.date.available2016-02-08T12:21:52Z
dc.date.issued2015en_US
dc.identifier.issn10586393
dc.identifier.urihttp://hdl.handle.net/11693/28485
dc.description.abstractA novel recursive framework for sparse reconstruction of continuous parameter spaces is proposed by adaptive partitioning and discretization of the parameter space together with expectation maximization type iterations. Any sparse solver or reconstruction technique can be used within the proposed recursive framework. Experimental results show that proposed technique improves the parameter estimation performance of classical sparse solvers while achieving Cramér-Rao lower bound on the tested frequency estimation problem. © 2014 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleConference Record - Asilomar Conference on Signals, Systems and Computersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ACSSC.2014.7094524en_US
dc.subjectbasis mismatchen_US
dc.subjectCompressive sensingen_US
dc.subjectoff-grid targetsen_US
dc.subjectrecursive solveren_US
dc.subjectsparse reconstructionen_US
dc.subjectChannel estimationen_US
dc.subjectCompressed sensingen_US
dc.subjectMaximum principleen_US
dc.subjectbasis mismatchen_US
dc.subjectCompressive sensingen_US
dc.subjectOff-gridsen_US
dc.subjectrecursive solveren_US
dc.subjectSparse reconstructionen_US
dc.subjectFrequency estimationen_US
dc.titleA recursive way for sparse reconstruction of parametric spacesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage637en_US
dc.citation.epage641en_US
dc.citation.volumeNumber2015-Aprilen_US
dc.identifier.doi10.1109/ACSSC.2014.7094524en_US
dc.publisherIEEE Computer Societyen_US


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