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Open AccessArticle
Improving Indoor WiFi Localization by Using Machine Learning Techniques
by
Hanieh Esmaeili Gorjan
Hanieh Esmaeili Gorjan and
Víctor P. Gil Jiménez
Víctor P. Gil Jiménez *
Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Av. de la Universidad, 30, Leganés, 28911 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(19), 6293; https://doi.org/10.3390/s24196293 (registering DOI)
Submission received: 26 August 2024
/
Revised: 24 September 2024
/
Accepted: 25 September 2024
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Published: 28 September 2024
Abstract
Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.
Share and Cite
MDPI and ACS Style
Esmaeili Gorjan, H.; Gil Jiménez, V.P.
Improving Indoor WiFi Localization by Using Machine Learning Techniques. Sensors 2024, 24, 6293.
https://doi.org/10.3390/s24196293
AMA Style
Esmaeili Gorjan H, Gil Jiménez VP.
Improving Indoor WiFi Localization by Using Machine Learning Techniques. Sensors. 2024; 24(19):6293.
https://doi.org/10.3390/s24196293
Chicago/Turabian Style
Esmaeili Gorjan, Hanieh, and Víctor P. Gil Jiménez.
2024. "Improving Indoor WiFi Localization by Using Machine Learning Techniques" Sensors 24, no. 19: 6293.
https://doi.org/10.3390/s24196293
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