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Support Vector Regression to Accelerate Design and Crosspolar Optimization of Shaped-Beam Reflectarray Antennas for Space Applications

dc.contributor.authorRodríguez Prado, Daniel 
dc.contributor.authorLópez Fernández, Jesús Alberto 
dc.contributor.authorArrebola Baena, Manuel 
dc.contributor.authorGoussetis, George
dc.date.accessioned2019-05-13T06:22:20Z
dc.date.available2019-05-13T06:22:20Z
dc.date.issued2019-03
dc.identifier.citationIEEE Transactions on Antennas and Propagation, Vol 67(3), p. 1659 - 1668 (2019); doi:10.1109/TAP.2018.2889029
dc.identifier.issn0018-926X
dc.identifier.urihttp://hdl.handle.net/10651/51228
dc.description.abstractA machine learning technique is applied to the design and optimization of reflectarray antennas to considerably accelerate computing time without compromising accuracy. In particular, Support Vector Machines (SVMs), automatic learning structures that are able to deal with regression problems, are employed to obtain surrogate models of the reflectarray element to substitute the full-wave analysis tool for the characterization of the unit cell in the design and optimization algorithms. The analysis, design and optimization of a very large reflectarray antenna for Direct Broadcast Satellite applications are accelerated up to three orders of magnitude. This is here demonstrated with three examples: one showing the design of a reflectarray; and two for the crosspolar optimization, one with one coverage for each linear polarization (Europe and the Middle East) and another with a Middle East coverage working in dual-linear polarization. The accuracy of the proposed approach is validated by means of a comparison of the final designs with full-wave simulations based on local periodicity obtaining good agreement. The crosspolar dicrimination and crosspolar isolation are greatly improved using the SVMs while considerably reducing computing time.spa
dc.description.sponsorshipThis work was supported in part by the European Space Agency (ESA) under contract ESTEC/AO/1-7064/12/NL/MH; by the Ministerio de Ciencia, Innovación y Universidades under projects TEC2017-86619-R (ARTEINE); by the Ministerio de Economía, Industria y Competitividad under project TEC2016-75103-C2-1-R (MYRADA); and by the Gobierno del Principado de Asturias through Programa “Clarín” de Ayudas Postdoctorales / Marie Courie-Cofund under project ACA17-09.spa
dc.format.extentp. 1659 - 1668spa
dc.language.isoengspa
dc.relation.ispartofIEEE Transactions on Antennas and Propagation, Vol 67(3)spa
dc.rights© IEEE
dc.subjectMachine learning techniquesspa
dc.subjectSupport Vector Machine (SVM)spa
dc.subjectshaped beam antennaspa
dc.subjectantennaspa
dc.subjectsatellite communicationsspa
dc.subjectDirect Broadcast Satellite (DBS)spa
dc.subjectreflectarrayspa
dc.titleSupport Vector Regression to Accelerate Design and Crosspolar Optimization of Shaped-Beam Reflectarray Antennas for Space Applicationsspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.identifier.doi10.1109/TAP.2018.2889029
dc.type.dcmitextspa
dc.relation.projectIDESA/ESTEC/AO/1-7064/12/NL/MH
dc.relation.projectIDTEC2017-86619-R
dc.relation.projectIDMINECO/TEC2016-75103-C2-1-R
dc.relation.projectIDMarie Courie-Cofund/ACA17-09
dc.relation.publisherversionhttps://doi.org/10.1109/TAP.2018.2889029spa
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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