Visible Light and Inertial Navigation Fusion Indoor Positioning System Based on Hidden Markov Model

Chen Yong; Wu Jie; Liu Huanlin; Zheng Han

doi:10.3788/CJL202047.1206001

Chinese Journal of Lasers, Volume. 47, Issue 12, 1206001(2020)

Visible Light and Inertial Navigation Fusion Indoor Positioning System Based on Hidden Markov Model

Chen Yong^1、*, Wu Jie¹, Liu Huanlin², and Zheng Han¹

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

²Key Laboratory of Optical Fiber Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Abstract Get PDF(in Chinese)

Figures & Tables(14)

Fig. 1. Indoor positioning fingerprint map

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Fig. 2. Diagram of HMM

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Fig. 3. Diagram of candidate set of user maximum speed limit

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Fig. 4. Schematic diagrams of Viterbi algorithm based on user maximum speed. (a) Traditional Viterbi algorithm; (b) improved Viterbi algorithm

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Fig. 5. Flow chart of fusion positioning algorithm

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Fig. 6. Fingerprint map of indoor parking lot

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Fig. 7. Experimental trajectory of indoor parking lot

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Fig. 8. Cumulative error distribution function

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Fig. 9. Positioning track maps of four contrast algorithms. (a) Kalman filter based VLC and PDR fusion location trajectory map; (b) visible light fingerprint positioning trajectory; (c) traditional HMM VLC positioning trajectory; (d) inertial navigation positioning trajectory

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Fig. 10. CDF of positioning error of compared algorithms

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Fig. 11. Location error graph corresponding to different numbers of candidate nodes

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Table 1. Simulation parameters

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Table 1. Simulation parameters

Symbol	Parameter	Value
$S$	Room size /m²	2500
$H_{PD}$	Receiving height /m	1
$H_{LED}$	LED height /m	4
$N_{Led}$	Number of LEDs in each lamp	26
$P_{Pt}$	Transmitted power of lamp /W	5-10
$T_{Ts} (Ψ)$	Gain of optical filter	1
$g (Ψ)$	Gain of optical concentrator	1
$F_{FOV}$	Field-of-view /(°)	55
$A$	Physical area of photo-detector /cm²	1
$N_{nodes}$	Number of nodes	162
$V_{\max}$	Maximum speed /(m·s^-1)	5

Table 2. Positioning error statistics

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Table 2. Positioning error statistics

Number of experiments	Forecast numberof nodes	Accuracy of nodeprediction /%	Maximumerror /m	Minimumerror /m	Averageerror /m	Root meansquare error /m
10	3000	82	5.1	0.6	3.85	4.42
20	6000	85	5.4	0.3	3.47	3.95
30	9000	84	5.5	0.2	3.56	4.17

Table 3. Positioning error statistics of compared algorithms

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Table 3. Positioning error statistics of compared algorithms

Positioningmethod	Maximumerror /m	Minimumerror /m	Averageerror /m	Root meansquare error /m	Medianerror /m
VLCFM^[9]	7.3	0.10	4.61	5.10	4.22
VLC-HMM	7.4	0.30	5.52	6.13	5.04
INPM^[10]	9.6	0.05	6.62	7.14	5.56
KF VLC-IN^[11]	6.5	0.60	4.27	4.85	3.74
Proposed method	5.5	0.20	3.35	3.88	2.92

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Chen Yong, Wu Jie, Liu Huanlin, Zheng Han. Visible Light and Inertial Navigation Fusion Indoor Positioning System Based on Hidden Markov Model[J]. Chinese Journal of Lasers, 2020, 47(12): 1206001

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Paper Information

Category: Fiber optics and optical communication

Received: Jun. 4, 2020

Accepted: --

Published Online: Nov. 26, 2020

The Author Email: Yong Chen (chenyong@cqupt.edu.cn)

DOI:10.3788/CJL202047.1206001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology