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
- Chinese Journal of Lasers
- Vol. 49, Issue 21, 2106001 (2022)
Fig. 1. Indoor visible light positioning model
Fig. 2. ELM network structure
Fig. 3. Genetic algorithm flow chart
Fig. 4. GA-ELM algorithm flow chart
Fig. 5. Indoor three-dimensional localization distribution map
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
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
Fig. 8. Localization error cumulative distribution of the system when receiver is at different altitudes
Fig. 9. Experimental scene and receiving end equipment. (a) Experimental scene; (b) receiving end equipment
Fig. 10. Two-dimensional localization results of GA-ELM
Fig. 11. Localization error histogram
Fig. 12. Cumulative distribution of localization error of GA-ELM and ELM 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 2. Simulation parameters
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Table 3. Simulation results for different training data sets
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Table 4. Maximum localization error for random 10 estimation results
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Table 5. Comparison of localization errors of different algorithms
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Table 6. Comparison of localization timeliness of different algorithms
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