Laser & Optoelectronics Progress, Volume. 59, Issue 3, 0304002(2022)

Indoor Visible Light Positioning Method Based on Extreme Learning Machine Neural Network

Ling Qin, Dongxing Wang, Fengying Wang, and Xiaoli Hu*
Author Affiliations
  • School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou , Inner Mongolia 014010, China
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    References(19)

    [1] Zuo X. Research on indoor positioning algorithm based on visible light[D], 20-28(2020).

    [2] Shi M Q, Cui L Z, Qin L et al. Single LED visible light indoor positioning system based on particle swarm optimization[J]. Laser Journal, 41, 31-34(2020).

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    [5] Zhao K, Hu H F. Indoor localization algorithm based on BP neural network and RFID[J]. Laser Journal, 36, 138-141(2015).

    [9] Xu S W, Wu Y, Wang X F. Visible light positioning algorithm based on sparsity adaptive and location fingerprinting[J]. Acta Optica Sinica, 40, 1806003(2020).

    [10] Liu K H, Yan S D, Gong X L. Indoor 3D visible light positioning algorithm based on fingerprint reconstruction and sparse training nodes[J]. Chinese Journal of Lasers, 48, 0306003(2021).

    [11] Zou P, Zhao Y H, Hu F C et al. Research status of machine learning based signal processing in visible light communication[J]. Laser & Optoelectronics Progress, 57, 010001(2020).

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    [13] Sun Y B, Huang Z, Gao R X et al. Visible light indoor localization system based on RSS[J]. Instrument Technique and Sensor, 80-84(2018).

    [18] Xiao J L, Yue D W, Zhao Z D et al. A visible light localization algorithm based on BP neural network optimized by genetic algorithm[J]. Journal of Optoelectronics·Laser, 30, 810-816(2019).

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    Ling Qin, Dongxing Wang, Fengying Wang, Xiaoli Hu. Indoor Visible Light Positioning Method Based on Extreme Learning Machine Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(3): 0304002

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    Paper Information

    Category: Detectors

    Received: Apr. 20, 2021

    Accepted: May. 19, 2021

    Published Online: Jan. 24, 2022

    The Author Email: Xiaoli Hu (huxiaoli@imust.edu.cn)

    DOI:10.3788/LOP202259.0304002

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