Chinese Journal of Lasers, Volume. 48, Issue 3, 0306003(2021)

Indoor 3D Visible Light Positioning Algorithm Based on Fingerprint Reconstruction and Sparse Training Nodes

Kaihua Liu, Shudan Yan*, and Xiaolin Gong*
Author Affiliations
  • School of Microelectronics, Tianjin University, Tianjin 300072, China
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    Figures & Tables(17)
    Positioning system model
    Specific layout with boundary correction
    Distribution of three-dimensional positioning with ELM
    CDF of positioing error at the heights of 0,0.3,0.6,0.9 m
    CDF of positioing error under different signal-to-noise ratios
    CDF of simulation positioning error under the sparse samples with the number of 32,40,48,56,64
    CDF of simulation positioning error with adding 4 virtual nodes
    CDF of simulation positioning error with adding 8 virtual nodes
    Positioning error distribution with x, y, z axes. (a) 32 samples; (b)40 samples; (c) 48 samples; (d) 56 samples
    CDF of simulation positioning error with RF, KNN, BP, ELM, and SRoFM-LwBC, respectively
    Location distribution of estimated targets with ELM and SRoFM-LwBC, respectively
    Specific experimental layout under the visible light scene
    CDF comparison of experimental positioning error under sparse samples with SRoFM(The dotted lines are the simulation results, the solid lines are the experimental results)
    CDF comparison of experimental positioning error under sparse samples by introducing LwBC
    Positioning error distribution of x, y, z axes. (a) 40 samples; (b) 48 samples; (c) 56 samples
    • Table 1. Simulation parameters

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      Table 1. Simulation parameters

      ParameterValue
      Interior room size /(m×m×m)5×5×3
      Power of LED lights /W60
      Locations of LED lights /m(1.25,1.25,3), (-1.25,1.25,3),(-1.25,-1.25,3),(1.25,-1.25,3)
      Effective area of PD /cm21
      Ha Half power angle of transmitters /(°)70
      Field of view angle of receivers /(°)70
      Responsivity /(A·W-1)0.62
      Refractive index1.5
      Reflection coefficient of the wall0.8
    • Table 2. Computing resource and positioning error comparision of algorithms

      View table

      Table 2. Computing resource and positioning error comparision of algorithms

      ParameterRFKNNBPOnly ELMSRoFM-LwBC
      Average positioning time /s0.4930.0490.2390.03240.0205
      Average positioning error /cm16.12212.42611.3523.1451.593
      Average minimum error /cm0.047600.31560.19120.2430
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    Kaihua Liu, Shudan Yan, Xiaolin Gong. Indoor 3D Visible Light Positioning Algorithm Based on Fingerprint Reconstruction and Sparse Training Nodes[J]. Chinese Journal of Lasers, 2021, 48(3): 0306003

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    Paper Information

    Category: fiber optics and optical communications

    Received: Aug. 14, 2020

    Accepted: Sep. 7, 2020

    Published Online: Feb. 2, 2021

    The Author Email: Yan Shudan (2018232071@tju.edu.cn), Gong Xiaolin (2018232071@tju.edu.cn)

    DOI:10.3788/CJL202148.0306003

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