• Laser & Optoelectronics Progress
  • Vol. 59, Issue 2, 0215008 (2022)
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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    DOI: 10.3788/LOP202259.0215008 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Comparison before and after filtering. (a) Before filtering; (b) after filtering
    Fig. 1. Comparison before and after filtering. (a) Before filtering; (b) after filtering
    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
    Process of indoor positioning
    Fig. 3. Process of indoor positioning
    Schematic of GBDT
    Fig. 4. Schematic of GBDT
    Schematic diagram of fruit fly population foraging
    Fig. 5. Schematic diagram of fruit fly population foraging
    Flow chart of LLE+GBDT model
    Fig. 6. Flow chart of LLE+GBDT model
    Experimental environment. (a) East 302; (a) south 411
    Fig. 7. Experimental environment. (a) East 302; (a) south 411
    Experimental scene diagrams for different sampling intervals
    Fig. 8. Experimental scene diagrams for different sampling intervals
    CDF of different dimensionality reduction algorithms
    Fig. 9. CDF of different dimensionality reduction algorithms
    Positioning accuracy of different optimization algorithms
    Fig. 10. Positioning accuracy of different optimization algorithms
    Localization result CDF with missing values
    Fig. 11. Localization result CDF with missing values
    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 1. Comparison of positioning performances at different sampling intervals
    AlgorithmAverage positioning error /m
    PCA+GBDT0.262
    LLE+GBDT0.155
    ENLLE+GBDT0.068
    Table 2. Localization performance of different dimensionality reduction 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
    Table 3. Effect of missing values on positioning performance of different algorithms
    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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