• Chinese Optics Letters
  • Vol. 15, Issue 5, 050601 (2017)
Heqing Huang1、2, Aiying Yang1、*, Lihui Feng1, Guoqiang Ni1、2, and Peng Guo1
Author Affiliations
  • 1School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
  • 2Key Laboratory of Photo-electronic Imaging Technology and System (Beijing Institute of Technology), Ministry of Education of China, Beijing 100081, China
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    DOI: 10.3788/COL201715.050601 Cite this Article Set citation alerts
    Heqing Huang, Aiying Yang, Lihui Feng, Guoqiang Ni, Peng Guo. Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel[J]. Chinese Optics Letters, 2017, 15(5): 050601 Copy Citation Text show less
    Typical scenario of indoor visible light positioning.
    Fig. 1. Typical scenario of indoor visible light positioning.
    Geometry of a diffuse channel.
    Fig. 2. Geometry of a diffuse channel.
    Structure of an artificial neural network.
    Fig. 3. Structure of an artificial neural network.
    Block diagram of the proposed visible light positioning algorithm.
    Fig. 4. Block diagram of the proposed visible light positioning algorithm.
    Positioning error at sampled points of (a) typical RSS-based positioning algorithm and (b) proposed neural-network-based positioning algorithm.
    Fig. 5. Positioning error at sampled points of (a) typical RSS-based positioning algorithm and (b) proposed neural-network-based positioning algorithm.
    (a) Distribution of the positioning error on the receiver plane and (b) CDF of the positioning error with different times of iteration.
    Fig. 6. (a) Distribution of the positioning error on the receiver plane and (b) CDF of the positioning error with different times of iteration.
    Positioning error with a different number of hidden nodes.
    Fig. 7. Positioning error with a different number of hidden nodes.
    Positioning error with a different reflectivity of the wall.
    Fig. 8. Positioning error with a different reflectivity of the wall.
    Positioning error with a different FOV of the receiver.
    Fig. 9. Positioning error with a different FOV of the receiver.
    SymbolParameterValue
    PtOptical power of LEDs1 W
    Φ1/2LED semi-angle at half-power60°
    RWReflectivity of the wall0.7
    RResponsivity of the receiver0.5 A/W
    ArArea of the receiver1cm2
    nRefractive index1.5
    FOVField-of-view of the receiver70°
    T(φ)Transmission of the receiver1
    GOpen-loop voltage gain10
    ηFixed capacitance of the PD112pF/cm2
    ΓChannel noise factor1.5
    gmFET transconductance30 mS
    TkAbsolute temperature298 K
    BEquivalent noise bandwidth100 MHz
    LNumber of nodes in the hidden layer20
    Table 1. Key Parameters Used in Simulation
    Number of hidden nodes01015202530
    Average training time03 s5.3 s8.4 s8.8 s26.3 s
    Average positioning time5.34 ms75.3 μs75.1 μs76.1 μs77.9 μs75.5 μs
    Table 2. Average Training Time and Positioning Time with a Different Number of Hidden Nodes
    Heqing Huang, Aiying Yang, Lihui Feng, Guoqiang Ni, Peng Guo. Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel[J]. Chinese Optics Letters, 2017, 15(5): 050601
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