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Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0215008(2022)

Channel State Information Indoor Fingerprint Localization Algorithm Based on Locally Linear Embedding and Gradient Boosting Decision Tree

Xinchun Li1, Zhongting Zhao2、*, and Hongshi Yu1
Author Affiliations
  • 1School of Electronics and Information Engineering, Liaoning Technical University, Huludao , Liaoning 125105, China
  • 2Graduate School, Liaoning Technical University, Huludao , Liaoning 125105, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
    Equations (27)
    References (26)
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    Figures & Tables(14)
    Comparison before and after filtering. (a) Before filtering; (b) after filtering

    Fig. 1. Comparison before and after filtering. (a) Before filtering; (b) after filtering

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    Comparison before and after phase correction. (a) Before correction; (b) after correction

    Fig. 2. Comparison before and after phase correction. (a) Before correction; (b) after correction

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    Process of indoor positioning

    Fig. 3. Process of indoor positioning

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    Schematic of GBDT

    Fig. 4. Schematic of GBDT

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    Schematic diagram of fruit fly population foraging

    Fig. 5. Schematic diagram of fruit fly population foraging

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    Flow chart of LLE+GBDT model

    Fig. 6. Flow chart of LLE+GBDT model

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    Experimental environment. (a) East 302; (a) south 411

    Fig. 7. Experimental environment. (a) East 302; (a) south 411

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    Experimental scene diagrams for different sampling intervals

    Fig. 8. Experimental scene diagrams for different sampling intervals

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    CDF of different dimensionality reduction algorithms

    Fig. 9. CDF of different dimensionality reduction algorithms

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    Positioning accuracy of different optimization algorithms

    Fig. 10. Positioning accuracy of different optimization algorithms

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    Localization result CDF with missing values

    Fig. 11. Localization result CDF with missing values

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    • Table 1. Comparison of positioning performances at different sampling intervals

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      Table 1. Comparison of positioning performances at different sampling intervals

      Positioning performanceSampling interval /m
      1.20.90.6
      Positioning accuracy /%97.897.697.3
      Average positioning error /m0.0350.0470.063
      Training time /s1.7115.33345.98

    • Table 2. Localization performance of different dimensionality reduction algorithms

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      Table 2. Localization performance of different dimensionality reduction algorithms

      AlgorithmAverage positioning error /m
      PCA+GBDT0.262
      LLE+GBDT0.155
      ENLLE+GBDT0.068

    • Table 3. Effect of missing values on positioning performance of different algorithms

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      Table 3. Effect of missing values on positioning performance of different algorithms

      AlgorithmPositioning performanceNo missing valueWith missing value
      LLE+GBDTPositioning Accuracy /%98.697.2
      Average positioning error /m0.0390.128
      Joint fingerprint+KNNPositioning Accuracy /%97.895.6
      Average positioning error /m0.0480.184
      Amplitude fingerprint+PCA-DNNPositioning Accuracy /%97.393.8
      Average positioning error /m0.0560.235
      Phase fingerprint + WKNNAverage positioning error /m1.0144.877
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    Xinchun Li, Zhongting Zhao, Hongshi Yu. Channel State Information Indoor Fingerprint Localization Algorithm Based on Locally Linear Embedding and Gradient Boosting Decision Tree[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215008

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    Paper Information

    Category: Machine Vision

    Received: Jul. 12, 2021

    Accepted: Sep. 8, 2021

    Published Online: Dec. 29, 2021

    The Author Email: Zhongting Zhao (1213259067@qq.com)

    DOI:10.3788/LOP202259.0215008

    Topics

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