Chinese Journal of Lasers, Volume. 48, Issue 3, 0306003(2021)
Indoor 3D Visible Light Positioning Algorithm Based on Fingerprint Reconstruction and Sparse Training Nodes
Fig. 6. CDF of simulation positioning error under the sparse samples with the number of 32,40,48,56,64
Fig. 9. Positioning error distribution with x, y, z axes. (a) 32 samples; (b)40 samples; (c) 48 samples; (d) 56 samples
Fig. 10. CDF of simulation positioning error with RF, KNN, BP, ELM, and SRoFM-LwBC, respectively
Fig. 11. Location distribution of estimated targets with ELM and SRoFM-LwBC, respectively
Fig. 13. CDF comparison of experimental positioning error under sparse samples with SRoFM(The dotted lines are the simulation results, the solid lines are the experimental results)
Fig. 14. CDF comparison of experimental positioning error under sparse samples by introducing LwBC
Fig. 15. Positioning error distribution of x, y, z axes. (a) 40 samples; (b) 48 samples; (c) 56 samples
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Kaihua Liu, Shudan Yan, Xiaolin Gong. Indoor 3D Visible Light Positioning Algorithm Based on Fingerprint Reconstruction and Sparse Training Nodes[J]. Chinese Journal of Lasers, 2021, 48(3): 0306003
Category: fiber optics and optical communications
Received: Aug. 14, 2020
Accepted: Sep. 7, 2020
Published Online: Feb. 2, 2021
The Author Email: Yan Shudan (2018232071@tju.edu.cn), Gong Xiaolin (2018232071@tju.edu.cn)