Indoor positioning for smartphones without infrastructure and user adaptable

dc.contributor.advisorIglesias Rodríguez, Roberto
dc.contributor.advisorCanedo Rodríguez, Adrián
dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro Internacional de Estudos de Doutoramento e Avanzados (CIEDUS)
dc.contributor.affiliationUniversidade de Santiago de Compostela. Escola de Doutoramento Internacional en Ciencias e Tecnoloxíagl
dc.contributor.authorRodríguez García, Germán
dc.date.accessioned2019-12-17T09:52:21Z
dc.date.available2019-12-17T09:52:21Z
dc.date.issued2019
dc.description.abstractGiven that the classic solutions for positioning outdoors, such as GPS (Global Positioning System) or GNSS (Global Navigation Satellite System) do not work indoors, there have been emerging multiple alternatives for Indoor Location. Usually these solutions require extensive and complex installations, which involve high costs. In this thesis we present a robust indoor positioning solution for smartphones that maximizes location accuracy while minimizes the required infrastructure. We have considered two main modes of displacement: walking and in a vehicle. Our solution is robust to different users, allows them to carry the phone in different positions and allows to use the device freely while performing different daily activities, such as walking, driving , going up and down stairs, etc. We achieved that by developing a robust indoor positioning system that combines information from multiple sources such as radio frequency readings and inertial sensors.gl
dc.description.programaUniversidade de Santiago de Compostela. Programa de Doutoramento en Investigación en Tecnoloxías da Información
dc.identifier.urihttp://hdl.handle.net/10347/20494
dc.language.isoenggl
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.accessRightsopen accessgl
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIndoor Locationgl
dc.subjectParticle Filtergl
dc.subjectMachine Learninggl
dc.subject.classificationMaterias::Investigación::12 Matemáticas::1203 Ciencia de los ordenadores::120304 Inteligencia artificialgl
dc.titleIndoor positioning for smartphones without infrastructure and user adaptablegl
dc.typedoctoral thesisgl
dspace.entity.typePublication
relation.isAdvisorOfPublication99ba5c78-bd31-4c8b-976f-b495174c8099
relation.isAdvisorOfPublication.latestForDiscovery99ba5c78-bd31-4c8b-976f-b495174c8099

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