Improving Indoor WiFi Localization by Using Machine Learning Techniques
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作者:
Gorjan, Hanieh Esmaeili
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Univ Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, SpainUniv Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, Spain
Gorjan, Hanieh Esmaeili
[1
]
Jimenez, Victor P. Gil
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机构:
Univ Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, SpainUniv Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, Spain
Jimenez, Victor P. Gil
[1
]
机构:
[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, Spain
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.