3-D Indoor Visible Light Positioning (VLP) System based on Linear Regression or Kernel Ridge Regression Algorithms

Dong-Chang Lin,Chi-Wai Chow,Chien-Hung Yeh,Yang Liu,Shao-Hua Song,Yun-Shen Lin,Chong-You Hong,Yu-Chun Wu

doi:10.1109/gcwkshps50303.2020.9367514

3-D Indoor Visible Light Positioning (VLP) System based on Linear Regression or Kernel Ridge Regression Algorithms

Dong-Chang Lin, Chi-Wai Chow + Show 6 more

https://doi.org/10.1109/gcwkshps50303.2020.9367514

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Publication Date: Dec 1, 2020

Citations: 5

Affiliation: National Yang Ming Chiao Tung University, Feng Chia University, Philips (China)

#Indoor Visible Light Positioning #Visible Light Positioning + Show 8 more

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Abstract

We proposed and demonstrated a 3-D indoor visible light positioning (VLP) system based on received signal strength (RSS) technique. To enhance the positioning accuracy, linear regression (LR) and kernel ridge regression (KRR) were employed. Here, we experimentally compared both schemes, and reported that the KRR scheme outperformed the LR scheme.

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