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Improving Indoor WiFi Localization by Using Machine Learning Techniques
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DOI：10.3390/s24196293.png)
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Abstract
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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.
Keywords:
WiFi positioning
machine learning
random forest
KNN
NN
catBoost
XGBoost
GridSearchCV
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