Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1949
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dc.contributor299983es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7635-4687-
dc.coverage.spatialGlobales_ES
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorGarcía Vázquez, Juan Pablo-
dc.creatorBrena, Ramón-
dc.date.accessioned2020-05-25T19:38:34Z-
dc.date.available2020-05-25T19:38:34Z-
dc.date.issued2014-03-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn1424-8220es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1949-
dc.identifier.urihttps://doi.org/10.48779/k5r6-7m05-
dc.descriptionUser indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.es_ES
dc.description.abstractUser indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationhttps://www.mdpi.com/1424-8220/14/6/11001/htmes_ES
dc.relation.urigeneralPublices_ES
dc.sourceSensors, Vol. 14, No. 6, pp. 1003-11015es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherindoor locationes_ES
dc.subject.othermagnetic field measurementes_ES
dc.subject.othermobile sensorses_ES
dc.subject.othermagnetometeres_ES
dc.subject.otherindoor positioninges_ES
dc.subject.otherlocation estimationes_ES
dc.subject.otherfeature extractiones_ES
dc.subject.otherfeature extractiones_ES
dc.titleMagnetic Field Feature Extraction and Selection for Indoor Location Estimationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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