A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting
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Título
A Generative Method for Indoor Localization Using Wi-Fi FingerprintingAutoría
Fecha de publicación
2021-03-30Editor
MDPIISSN
1424-8220Cita bibliográfica
Belmonte-Fernández, Óscar; Sansano-Sansano, Emilio; Caballer-Miedes, Antonio; Montoliu, Raúl; García-Vidal, Rubén; Gascó-Compte, Arturo. 2021. "A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting" Sensors 21, no. 7: 2392. https://doi.org/10.3390/s21072392Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/journal/sensorsVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used ... [+]
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most
Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We
present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received
Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov
model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage
of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s
location based on the hidden Markov model, which models the signal and the forward algorithm to
determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed
method was compared with four other well-known Machine Learning algorithms through extensive
experimentation with data collected in real scenarios. The proposed method obtained competitive
results in most scenarios tested and was the best method in 17 of 60 experiments performed. [-]
Publicado en
Sensors 2021, 21(7), 2392; https://doi.org/10.3390/s21072392Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades (Spain) | Universitat Jaume I | Generalitat Valenciana
Código del proyecto o subvención
RTI2018-095168-B-C53 | UJI-B2020-36 | AICO/2020/046 | PRX18/00123
Derechos de acceso
info:eu-repo/semantics/openAccess
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