dc.contributor.authorZhao, Lifan
dc.contributor.authorWang, Lu
dc.contributor.authorYang, Lei
dc.contributor.authorZoubir, Abdelhak M.
dc.contributor.authorBi, Guoan
dc.date.accessioned2016-12-22T07:43:55Z
dc.date.available2016-12-22T07:43:55Z
dc.date.issued2016
dc.identifier.citationZhao, L., Wang, L., Yang, L., Zoubir, A. M., & Bi, G. (2016). The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques. IEEE Signal Processing Magazine, 33(6), 85-102.en_US
dc.identifier.issn1053-5888en_US
dc.identifier.urihttp://hdl.handle.net/10220/41933
dc.description.abstractThe exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to further performance benefits by imposing sparsity as a statistical prior on the considered signal. In this article, a comprehensive survey is made of recent progress on statistical sparsity based techniques for various radar imagery applications.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)
dc.format.extent35 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Signal Processing Magazineen_US
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/MSP.2016.2573847].en_US
dc.subjectStatistical analysisen_US
dc.subjectImage qualityen_US
dc.titleThe Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniquesen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/MSP.2016.2573847
dc.description.versionAccepted versionen_US


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