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Chinese Journal of Lasers, Volume. 49, Issue 21, 2106001(2022)

Indoor Visible Light Localization System Based on Genetic Algorithm-Optimized Extreme Learning Machine Neural Network

Ling Qin, Dongxing Wang, Mingquan Shi, Fengying Wang, and Xiaoli Hu*
Author Affiliations
  • School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (19)
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    References (22)
    Cited By (7)
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    Figures & Tables(19)
    Indoor visible light positioning model

    Fig. 1. Indoor visible light positioning model

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    ELM network structure

    Fig. 2. ELM network structure

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    Genetic algorithm flow chart

    Fig. 3. Genetic algorithm flow chart

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    GA-ELM algorithm flow chart

    Fig. 4. GA-ELM algorithm flow chart

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    Indoor three-dimensional localization distribution map

    Fig. 5. Indoor three-dimensional localization distribution map

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    Actual coordinates versus system predicted coordinates when receiver is at different altitudes. (a) 0.2 m; (b) 0.4 m; (c) 0.6 m; (d) 0.8 m

    Fig. 6. Actual coordinates versus system predicted coordinates when receiver is at different altitudes. (a) 0.2 m; (b) 0.4 m; (c) 0.6 m; (d) 0.8 m

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    Three-dimensional localization error of system when receiver is at different altitudes. (a) 0.2 m; (b) 0.4 m; (c) 0.6 m; (d) 0.8 m

    Fig. 7. Three-dimensional localization error of system when receiver is at different altitudes. (a) 0.2 m; (b) 0.4 m; (c) 0.6 m; (d) 0.8 m

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    Localization error cumulative distribution of the system when receiver is at different altitudes

    Fig. 8. Localization error cumulative distribution of the system when receiver is at different altitudes

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    Experimental scene and receiving end equipment. (a) Experimental scene; (b) receiving end equipment

    Fig. 9. Experimental scene and receiving end equipment. (a) Experimental scene; (b) receiving end equipment

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    Two-dimensional localization results of GA-ELM

    Fig. 10. Two-dimensional localization results of GA-ELM

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    Localization error histogram

    Fig. 11. Localization error histogram

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    Cumulative distribution of localization error of GA-ELM and ELM algorithms

    Fig. 12. Cumulative distribution of localization error of GA-ELM and ELM algorithms

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    Cumulative distribution of localization error of four algorithms

    Fig. 13. Cumulative distribution of localization error of four algorithms

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    • Table 1. Simulation results for selecting the number of LED

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      Table 1. Simulation results for selecting the number of LED

      The number of LEDMax localization error /cmAverage localization error /cmLocalization time /s
      399.49010.35000.0315
      43.9120.94180.0413
      64.6400.95000.0874

    • Table 2. Simulation parameters

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      Table 2. Simulation parameters

      ParameterValue
      Light source emission power /W10
      Receiver field of view ψc /(°)90
      Filter gain Ts(ψ)1
      Concentrator gain g(ψ)10
      Effective receiving area of receiver /cm21
      Angle of half-power ϕ1/2/(°)30
      Number of neurons225
      Population size2
      Chromosome length1300
      Maximum number of iteration200
      Crossover probability0.7
      Mutation probability0.01

    • Table 3. Simulation results for different training data sets

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      Table 3. Simulation results for different training data sets

      Data setAverage localization error /cmLocalization time /s
      11×1174.68000.00215
      21×210.92140.04235
      41×410.91380.10940

    • Table 4. Maximum localization error for random 10 estimation results

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      Table 4. Maximum localization error for random 10 estimation results

      No.Maximum localization error /cm
      GA-ELMELM
      14.109.76
      25.5122.21
      34.6813.25
      45.2710.63
      55.1012.42
      64.5012.22
      74.3211.88
      85.7816.29
      93.7215.13
      105.8213.60
      Average value /cm4.8813.34
      Standard deviation /cm0.723.55

    • Table 5. Comparison of localization errors of different algorithms

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      Table 5. Comparison of localization errors of different algorithms

      Localization algorithmMax localization error /cmAverage localization error /cm
      GA-ELM3.91920.9214
      GA-BP15.333.72
      SVM19.513.74
      BP60.1821.04

    • Table 6. Comparison of localization timeliness of different algorithms

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      Table 6. Comparison of localization timeliness of different algorithms

      Localization algorithmAverage localization time /s
      GA-ELM0.04235
      GA-BP0.09237
      SVM0.09165
      BP0.09301
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    Ling Qin, Dongxing Wang, Mingquan Shi, Fengying Wang, Xiaoli Hu. Indoor Visible Light Localization System Based on Genetic Algorithm-Optimized Extreme Learning Machine Neural Network[J]. Chinese Journal of Lasers, 2022, 49(21): 2106001

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    Paper Information

    Category: Fiber optics and optical communication

    Received: Dec. 17, 2021

    Accepted: Feb. 22, 2022

    Published Online: Nov. 9, 2022

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

    DOI:10.3788/CJL202249.2106001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology