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A Single-Anchor Visible Light Positioning System Based on Fingerprinting and Deep Learning

Published: 20 August 2023 Publication History

Abstract

Due to severe signal obstruction, the global navigation satellite system is unable to work indoors. Visible light positioning, as an alternative technology for indoor positioning, has gained widespread attention in recent years due to its low cost and environmental friendliness. Among these, the visible light single anchor positioning method based on light-emitting diode arrays has shown great potential as it can simultaneously provide lighting and positioning. The rise of artificial intelligence has provided new methods for indoor positioning.This article focuses on the single anchor visible light fingerprinting-based positioning technology and uses a multi-layer perceptron-based method to maximize its performance. In addition, in terms of hardware design, we focus on improving the receiver's integration, making it applicable to a wider range of scenarios through size reduction and cost control. Finally, the designed hardware and the proposed method are evaluated in the space range of 320 cm* 560 cm* 270 cm. When compared with the traditional nearest neighbor, k-nearest neighbor, and weighted k-nearest neighbor methods, the experimental results show that the proposed method exhibits significant advantages in performance. The average positioning accuracy in the real scene can reach 34cm.

References

[1]
X. Cao, Y. Zhuang, X. Yang, X. Sun, and X. Wang, “A universal Wi-Fi fingerprint localization method based on machine learning and sample differences,” Satellite Navigation, vol. 2, pp. 1-15, 2021.
[2]
X. Wang, Y. Zhuang, Z. Zhang, X. Cao, F. Qin, X. Yang, X. Sun, M. Shi, and Z. Wang, “Tightly-Coupled Integration of Pedestrian Dead Reckoning and Bluetooth Based on Filter and Optimizer,” IEEE Internet of Things Journal, 2022.
[3]
Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 1963-1988, 2018.
[4]
A. Yassin, Y. Nasser, M. Awad, A. Al-Dubai, R. Liu, C. Yuen, R. Raulefs, and E. Aboutanios, “Recent advances in indoor localization: A survey on theoretical approaches and applications,” IEEE Communications Surveys Tutorials, vol. 19, no. 2, pp. 1327-1346, 2016.
[5]
X. Zhu, W. Qu, T. Qiu, L. Zhao, M. Atiquzzaman, and D. O. Wu, “Indoor intelligent fingerprint-based localization: Principles, approaches and challenges,” IEEE Communications Surveys Tutorials, vol. 22, no. 4, pp. 2634-2657, 2020.
[6]
F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An accurate visible light positioning system using regenerated fingerprint database based on calibrated propagation model,” IEEE Transactions on Instrumentation Measurement, vol. 68, no. 8, pp. 2714-2723, 2018.
[7]
Y. Yue, X. Zhao, and Z. Li, “Enhanced and facilitated indoor positioning by visible-light GraphSLAM technique,” IEEE Internet of Things Journal, vol. 8, no. 2, pp. 1183-1196, 2020.
[8]
J. J. Jiangsheng Zhou, Kai Zhao, Yuan Zhuang, “A Novel Single-Anchor-based Visible Light Positioning Approach,” in 2023 8th International Conference on Computer and Communication Systems (ICCCS), Guang Zhou, China, 2023.
[9]
W. XU Yan, “Indoor Positioning Algorithm of Subregional Visible Light Based on Multilayer ELM,” Journal of Hunan University Natural Sciences, vol. 46, no. 10, 2019.
[10]
P. Du, S. Zhang, C. Chen, H. Yang, W.-D. Zhong, R. Zhang, A. Alphones, and Y. Yang, “Experimental demonstration of 3D visible light positioning using received signal strength with low-complexity trilateration assisted by deep learning technique,” IEEE Access, vol. 7, pp. 93986-93997, 2019.
[11]
H. Q. Tran, and C. Ha, “Machine learning in indoor visible light positioning systems: A review,” Neurocomputing, 2022.

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    AI2A '23: Proceedings of the 2023 3rd International Conference on Artificial Intelligence, Automation and Algorithms
    July 2023
    199 pages
    ISBN:9798400707605
    DOI:10.1145/3611450
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 20 August 2023

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    Author Tags

    1. Keywords—indoor positioning
    2. Multi-Layer Perceptron(MLP)
    3. fingerprinting
    4. single anchor
    5. visible light positioning

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    • Special Fund of Anhui University of Science and Technology

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