Experimental Research on Visible Light Positioning Using Machine Learning and Multi-Photodiode

Fen Wei; Yi Wu; Shiwu Xu

doi:10.3788/LOP213084

Laser & Optoelectronics Progress, Volume. 60, Issue 7, 0723002(2023)

Experimental Research on Visible Light Positioning Using Machine Learning and Multi-Photodiode

Fen Wei^1,2,3,4, Yi Wu^1,3,4、*, and Shiwu Xu^1,5

¹Key Laboratory of Opto-Electronic Science and Technology for Medicine Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China

²Jinshan College of Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China

³Fujian Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian 350007, China

⁴Fujian Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China

⁵Concord University College, Fujian Normal University, Fuzhou 350117, Fujian, China

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Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Experimental setup of the VLP system with a multi-PD receiver

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Fig. 2. Time division multiplexing scheme

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Fig. 3. Single-hidden layer feedforward network with L hidden neurons

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Fig. 4. Conceptual architecture of positioning system. (a) 2-D positioning; (b) 3-D positioning

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Fig. 5. Positioning algorithm flow diagram

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Fig. 6. CDF of positioning error for different algorithms. (a) 2-D positioning; (b) 3-D positioning

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Fig. 7. APE of different algorithms. (a) 2-D positioning; (b) 3-D positioning

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Fig. 8. Impact of M on APE. (a) 2-D positioning; (b) 3-D positioning

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Fig. 9. Impact of N on APE. (a) 2-D positioning; (b) 3-D positioning

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Fig. 10. Impact of P_t on APE. (a) 2-D positioning; (b) 3-D positioning

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Table 1. Experimental parameter

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Table 1. Experimental parameter

Parameter	Reference
Indoor space unit size（L×W×H）/cm	100×100×150
Plane range of receiver /cm	（0，0）to（65，70）（resolution：5）
Transmitter power /W	5，6，7^{［45，58］}
Height of the receiver /cm	10，20，30
Position of four LEDs（x， y，z）/cm	LED1（-10，-10，120） LED2（80，-10，120） LED3（80，80，120） LED4（-10，80，120）
Distance between each LED /cm	90
The FOV of LED /（°）	60
Distance between each PD /cm	5
The FOV of PD /（°）	120
The effective area of PD /cm²	1

Table 2. Parameter of the four machine learning algorithms

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Table 2. Parameter of the four machine learning algorithms

Algorithm	Parameter
KNN	Distance metric：Euclidean distance；K=3
ELM	Number neurons in input，hidden and output：16， adaptive and 1；Activation function：Sigmoid
RF	Tree number：50；Weak classifier：Decision tree
AdaBoost	Learning cycle：100；Weak classifier：Decision tree

Table 3. APT of different algorithms
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Table 3. APT of different algorithms
Algorithm S-KNN/KNN S-ELM/ELM S-RF/RF S-AdaBoost/AdaBoost
2-D positioning APT /s 0.01/0.01 0.01/0.05 0.72/1.14 0.98/2.43
3-D positioning APT /s 0.02/0.07 0.05/0.32 5.45/6.82 4.45/18.41

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Fen Wei, Yi Wu, Shiwu Xu. Experimental Research on Visible Light Positioning Using Machine Learning and Multi-Photodiode[J]. Laser & Optoelectronics Progress, 2023, 60(7): 0723002

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