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

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dc.contributor.author Villalón-Turrubiates, Iván E.
dc.contributor.author Shkvarko, Yuriy
dc.date.accessioned 2016-04-21T17:34:38Z
dc.date.available 2016-04-21T17:34:38Z
dc.date.issued 2005-12
dc.identifier.citation Yuriy 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.isbn 0-7803-9322-8
dc.identifier.uri http://hdl.handle.net/11117/3301
dc.description In 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.sponsorship Cinvestav es
dc.language.iso eng es
dc.publisher Institute of Electrical and Electronics Engineers es
dc.relation.ispartofseries IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP);1st
dc.rights.uri http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf es
dc.subject Signal Processing es
dc.subject Image Reconstruction es
dc.subject Neural Networks es
dc.title Unified Bayesian-Experiment Design Regularization Technique for High-Resolution of the Remote Sensing Imagery es
dc.type info:eu-repo/semantics/conferencePaper es
rei.revisor 1st IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP)
rei.peerreviewed Yes es


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