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Improved indoor visible light positioning system using machine learning
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DOI：10.1007/s11082-022-04482-1.png)
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Abstract
En 中文
In this study, we present a novel Visible Light Positioning (VLP) method to reduce the localization error in an indoor environment. Machine Learning (ML) methods including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs) are used in combination with the LED Received Signal Strength (RSS) and the angle of a steerable laser. Zemax optics studio simulator is used to build a real indoor scene. Orange data mining software is utilized to apply ML techniques. Our numerical findings show that the suggested system outperforms the other RSS Visible Light Communication (VLC)-based models by reducing the localization error by more than 90% in some areas.
Keywords:
Indoor localization
Machine Learning (ML)
Received Signal Strength (RSS)
Ray Tracing
Visible Light Positioning (VLP)
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4
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10.0K
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Cited Papers
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