Continuous space estimation: increasing WiFi-based indoor localization resolution without increasing the site-survey effort
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/59337DOI: 10.3390/s17010147
ISSN: 1424-8220
Publisher
MDPI
Date
2017-01-13Funders
Ministerio de Economía y Competitividad
Bibliographic citation
Hernández Parra, N., Ocaña Miguel, M., Alonso Moral, J.M. & Euntai, K. 2017, "Continuous space estimation: increasing WiFi-based indoor localization resolution without increasing the site-survey effort", Sensors, vol. 17, no. 1, art. no. 147, pp. 1-23.
Keywords
WiFi indoor localization
Fingerprinting
Continuous space estimation
Machine learning
Location-based services
Project
info:eu-repo/grantAgreement/MINECO//TIN2014-56633-C3-3-R/ES/ABSTRACCION, SINTESIS E INTEGRACION DE INFORMACION MULTISENSORIAL PARA LA LOCALIZACION, SEGUIMIENTO Y RECONOCIMIENTO DEL COMPORTAMIENTO DE CONSUMIDORES EN CENTROS COMERCIALES/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s17010147Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
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