Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting
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Otros documentos de la autoría: Mendoza-Silva, Germán Martín; Costa, Ana Cristina; Torres-Sospedra, Joaquín; Painho, Marco; Huerta, Joaquin
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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INVESTIGACIONMetadatos
Título
Environment-Aware Regression for Indoor Localization based on WiFi FingerprintingAutoría
Fecha de publicación
2021-04-19Editor
Institute of Electrical and Electronics Engineers; IEEEISSN
1530-437X; 1558-1748Cita bibliográfica
MENDOZA-SILVA, Germán, et al. Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting. IEEE Sensors Journal, 2021.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples, ... [+]
Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples, and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy. [-]
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