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Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method
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DOI：10.1109/ACCESS.2020.3041192.png)
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Abstract
En 中文
In this work, we propose and demonstrate a received-signal-strength (RSS) based visible-light-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions.
Keywords:
Three-dimensional displays
Radio frequency
Machine learning algorithms
Light emitting diodes
Machine learning
Two dimensional displays
Frequency modulation
Visible light communication (VLC)
visible light positioning (VLP)
light-emitting-diode (LED)
machine learning
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