Improving Indoor WiFi Localization by Using Machine Learning Techniques

被引:0
|
作者
Gorjan, Hanieh Esmaeili [1 ]
Jimenez, Victor P. Gil [1 ]
机构
[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, Ave Univ 30, Leganes 28911, Madrid, Spain
关键词
WiFi positioning; machine learning; random forest; KNN; NN; catBoost; XGBoost; GridSearchCV;
D O I
10.3390/s24196293
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:21
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