Attention Mechanism for Visible Light Positioning Unit Model Replication

Chuangshi Wang; Yong Chen; Huanlin Liu; Jinlan Wu; Hao Chen; Weiwei Zhang

doi:10.3788/CJL231279

Chinese Journal of Lasers, Volume. 51, Issue 8, 0806002(2024)

Attention Mechanism for Visible Light Positioning Unit Model Replication

Chuangshi Wang^1、**, Yong Chen^1、*, Huanlin Liu², Jinlan Wu¹, Hao Chen¹, and Weiwei Zhang¹

¹Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

²School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Figures & Tables(16)

Fig. 1. Indoor positioning simulation model

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Fig. 2. Top view of simulation positioning model

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Fig. 3. Top view of simulation positioning units (circular and rectangular dots are training and test points, respectively)

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Fig. 4. Architecture of attention CNN

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Fig. 5. Attention network architecture (ECANet)

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Fig. 6. Simulation error distributions before transfer learning. (a) Positioning of attention CNN; (b) positioning of CNN

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Fig. 7. Simulation positioning error cumulative distribution function diagram

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Fig. 8. Positioning error distributions when attention CNN was deployed to other positioning units. (a) Positioning of attention CNN in unit 2; (b) positioning of attention CNN in unit 3

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Fig. 9. Cumulative error function diagram of the network copied into the new location cell using transfer learning and unupdated parameters

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Fig. 10. Convergence comparison between attention CNN with transfer learning and CNN

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Fig. 11. Indoor positioning experiment photo

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Fig. 12. Experimental positioning results before transfer learning. (a) Positioning of attention CNN; (b) positioning of CNN; (c) positioning of RCNN^[17]

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Fig. 13. Experimental positioning results after transfer learning. (a) Positioning of attention CNN; (b) positioning of CNN; (c) positioning of RCNN

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Table 1. Parameters of simulation model

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Table 1. Parameters of simulation model

Parameter	Value
Positioning space size	10 m×10 m×3 m
Unit size	5 m×5 m×3 m
Transmitted power of a lamp	$P_{t}$ =5 W
Semi-angle at a half power	$Φ_{1 / 2}$ =60°
Gain of optical filter	$T_{s} (ψ)$ =1
Gain of optical concentrator	$g (ψ)$ =1
Physical area of photo-detector	$A_{P D}$ =1 cm²
Position of LEDs	(0 m, 0 m, 3 m), (0 m, 10 m,3 m), (2.5 m, 5 m, 3 m),(5 m, 3 m, 3 m), (5 m,7.5 m, 3 m), (8 m, 5 m,3 m), (10 m, 0 m, 3 m),(10 m, 10 m, 3 m)
Field of view	θ_FOV=60°
Refractive index	n=1.5
Wall reflection coefficient	$ρ = 0.6$
Signal-to-noise ratio	S_SNR=20 dB‒29 dB

Table 2. Experimental parameters

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Table 2. Experimental parameters

Parameter	Value
Positioning space size	1.5 m×1.2 m×1.2 m
Unit size	0.75 m×1.2 m×1.2 m
Transmitted power of a lamp	$P_{t}$ =5 W
Semi-angle at a half power	$Φ_{1 / 2}$ =60°
Gain of optical filter	$T_{s} (ψ)$ =1
Gain of optical concentrator	$g (ψ)$ =1
Physical area of photo-detector	$A_{P D}$ =12.96 mm²
Position of LED_s in unit 1	（0 mm， 0 mm， 1.2 mm），（0 mm， 1.2 mm， 1.2 mm），（0.75 mm， 0.6 mm， 1.2 mm）
Position of LED_s in unit 2	（0.75 mm， 0.6 mm， 1.2 mm），（1.5 mm， 0 mm， 1.2 mm），（1.5 mm， 1.2 mm， 1.2 mm）
Field of view	θ_FOV=60°
Signal-to-noise ratio	S_SNR=20 dB

Table 3. Comparison of average positioning error of different algorithms
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Table 3. Comparison of average positioning error of different algorithms
Algorithm Average positioning error in unit 1 /cm Average positioning error in unit 2 /cm
Attention CNN 3.32 3.35
CNN 4.02 4.04
RCNN^［17］ 4.69 5.64

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Chuangshi Wang, Yong Chen, Huanlin Liu, Jinlan Wu, Hao Chen, Weiwei Zhang. Attention Mechanism for Visible Light Positioning Unit Model Replication[J]. Chinese Journal of Lasers, 2024, 51(8): 0806002

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