Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting
| Autor: | Marco Painho, Germán Martín Mendoza-Silva, Joaquín Torres-Sospedra, Ana Cristina Costa, Joaquín Huerta |
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| Přispěvatelé: | NOVA IMS Research and Development Center (MagIC), NOVA Information Management School (NOVA IMS), Information Management Research Center (MagIC) - NOVA Information Management School |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Extrapolation Libraries RSS Regression computer.software_genre Signal strength Training Point (geometry) Data enrichment Analytical models WiFi Fingerprinting Wireless fidelity Electrical and Electronic Engineering WiFi Samples Collection Instrumentation Sensors SDG 8 - Decent Work and Economic Growth Regression Interpolation Support vector machine Indoor Positioning Data mining Sample collection computer |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | Mendoza-Silva, G., Costa, A. C., Torres-Sospedra, J., Painho, M., & Huerta, J. (2022). Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting. IEEE Sensors Journal, 22(6), 4978 - 4988. https://doi.org/10.1109/JSEN.2021.3073878 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. authorsversion published |
| Databáze: | OpenAIRE |
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