Unified Bayesian-Experiment Design Regularization Technique for High-Resolution of the Remote Sensing Imagery

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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.contributor.authorShkvarko, Yuriy
dc.date.accessioned2016-04-21T17:34:38Z
dc.date.available2016-04-21T17:34:38Z
dc.date.issued2005-12
dc.descriptionIn this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill- posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.es
dc.description.sponsorshipCinvestaves
dc.identifier.citationYuriy V. Shkvarko, Ivan E. Villalon-Turrubiates, “Unified Bayesian-Experiment Design Regularization Technique for High-Resolution of the Remote Sensing Imagery”, in Proceedings of the 1st IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP), Puerto Vallarta México, 2005, pp. 165-168.es
dc.identifier.isbn0-7803-9322-8
dc.identifier.urihttp://hdl.handle.net/11117/3301
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofseriesIEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP);1st
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectSignal Processinges
dc.subjectImage Reconstructiones
dc.subjectNeural Networkses
dc.titleUnified Bayesian-Experiment Design Regularization Technique for High-Resolution of the Remote Sensing Imageryes
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisor1st IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP)

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