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Please use this identifier to cite or link to this item:
http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1949
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | 299983 | es_ES |
dc.contributor.other | https://orcid.org/0000-0002-7635-4687 | - |
dc.coverage.spatial | Global | es_ES |
dc.creator | Galván Tejada, Carlos Eric | - |
dc.creator | García Vázquez, Juan Pablo | - |
dc.creator | Brena, Ramón | - |
dc.date.accessioned | 2020-05-25T19:38:34Z | - |
dc.date.available | 2020-05-25T19:38:34Z | - |
dc.date.issued | 2014-03-10 | - |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.issn | 1424-8220 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1949 | - |
dc.identifier.uri | https://doi.org/10.48779/k5r6-7m05 | - |
dc.description | User 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.abstract | User 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.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | https://www.mdpi.com/1424-8220/14/6/11001/htm | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.source | Sensors, Vol. 14, No. 6, pp. 1003-11015 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | indoor location | es_ES |
dc.subject.other | magnetic field measurement | es_ES |
dc.subject.other | mobile sensors | es_ES |
dc.subject.other | magnetometer | es_ES |
dc.subject.other | indoor positioning | es_ES |
dc.subject.other | location estimation | es_ES |
dc.subject.other | feature extraction | es_ES |
dc.subject.other | feature extraction | es_ES |
dc.title | Magnetic Field Feature Extraction and Selection for Indoor Location Estimation | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
Appears in Collections: | *Documentos Académicos*-- M. en Ciencias del Proc. de la Info. |
Files in This Item:
File | Description | Size | Format | |
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sensors-14-11001.pdf | 832,37 kB | Adobe PDF | View/Open |
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