dc.contributor.authorYang, Lei
dc.contributor.authorZhao, Lifan
dc.contributor.authorBi, Guoan
dc.contributor.authorZhang, Liren
dc.date.accessioned2017-10-17T08:51:46Z
dc.date.available2017-10-17T08:51:46Z
dc.date.issued2016
dc.identifier.citationYang, L., Zhao, L., Bi, G., & Zhang, L. (2016). SAR Ground Moving Target Imaging Algorithm Based on Parametric and Dynamic Sparse Bayesian Learning. IEEE Transactions on Geoscience and Remote Sensing, 54(4), 2254-2267.en_US
dc.identifier.issn0196-2892en_US
dc.identifier.urihttp://hdl.handle.net/10220/43922
dc.description.abstractIn this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time-frequency representation, which is known as Lv's distribution (LVD), is employed on the moving targets to determine the parametric dictionary used in the SBL framework. To combat the inherent accuracy limitations of the LVD and extrinsic perturbation errors, a dynamical refinement process is further developed and incorporated into the SBL framework to obtain highly focused SAR image of multiple moving targets. An emerging inference technique, which is known as variational Bayesian expectation-maximization, is applied to achieve an efficient Bayesian inference for the focused SAR moving target image. A remarkable advantage of the proposed algorithm is to provide a fully posterior distribution (Bayesian inference) for the SAR moving target image, rather than a poor point estimate used in conventional methods. Because of utilizing high-order statistical information, the error propagation problem is desirably ameliorated in an iterative manner. The perturbations, known as the multiplicative phase error and additive clutter and noise, are both well adjusted for further improving the image quality. Experimental results by using simulated spotlight-SAR data and real Gotcha data have demonstrated the superiority of the proposed algorithm over other reported ones.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Transactions on Geoscience and Remote Sensingen_US
dc.rights© 2016 IEEEen_US
dc.subjectGround moving target imagingen_US
dc.subjectLv’s distributionen_US
dc.titleSAR Ground Moving Target Imaging Algorithm Based on Parametric and Dynamic Sparse Bayesian Learningen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TGRS.2015.2498158


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