Skip to main content
SpringerLink
Log in

High-Accuracy 3D Indoor Visible Light Positioning Method Based on the Improved Adaptive Cuckoo Search Algorithm

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In visible light positioning(VLP) system, in the moving process of photodiode(PD), PD will rotate a small angle to a certain extent, although the rotation angle is small, it will still cause a large positioning error, thus, in order to alleviate the error caused by the rotation of PD, a high-precision 3D indoor VLP method based on the improved adaptive cuckoo search (VLP-IACS) algorithm is proposed in this paper. Firstly, the rotation angles of the photodiode (PD) are introduced into the optical channel transmission model instead of assuming that the PD and the light-emitting diode (LED) are parallel to each other. Secondly, two adaptive strategies are applied to update the detection probability \(p_{a}\) and step factor \(\alpha_{0}\) in the traditional cuckoo search (CS) algorithm, and the convergence speed of the cuckoo search algorithm is significantly enhanced. Finally, the IACS algorithm is successfully applied to solve the 3D indoor positioning problem in an indoor space with dimensions of 5 m \(\times\) 5 m \(\times\) 6 m. Simulation results for fixed positioning show that in the case of no PD rotation, the average 3D positioning error is 2.20 cm, and in the case of PD rotation, the average 3D positioning errors under different rotation angle ranges are 9.04 cm, 14.45 cm and 16.22 cm. The results of kinematic positioning show that in the case of no PD rotation, the average 3D positioning error is 1.54 cm, and in the case of PD rotation, the average 3D positioning error is 16.48 cm. The proposed method can effectively reduce the degradation caused by PD rotation in the positioning system and can potentially be used in various indoor positioning scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Abmm, R.; Li, T.; Wang, Y.: Recent advances in indoor localization via visible lights: a survey [J]. Sensors (Basel) 20(5), 1–27 (2020)

    Google Scholar 

  2. Huynh, P.; Yoo, M.: VLC-based positioning system for an indoor environment using an image sensor and an accelerometer sensor [J]. Sensors (Basel) 16(6), 783–798 (2016)

    Article  Google Scholar 

  3. Davidson, P.; Robert, P.: A survey of selected indoor positioning methods for smartphones[J]. IEEE Commun. Surv. Tutorials 19(2), 1347–1370 (2017)

    Article  Google Scholar 

  4. Steendam, H.; Wang, T.Q.; Armstrong, J.: Theoretical lower bound for indoor visible light positioning using received signal strength measurements and an aperture-based receiver[J]. J. Lightwave Technol. 35(2), 309–319 (2017)

    Article  Google Scholar 

  5. Lin, P.; Hu, X.; Ruan, Y., et al.: Real-time visible light positioning supporting fast moving speed[J]. Opt. Exp. 28(10), 14503–14510 (2020)

    Article  Google Scholar 

  6. Tanaka T., Haruyama S., New position detection method using image sensor and visible light LEDs, in IEEE International Conference on Machine Visions. 2009. 150–153.

  7. Huang, H.; Yang, A.; Feng, L., et al.: Indoor positioning method based on metameric white light sources and subpixels on a color image sensor[J]. IEEE Photonics J. 8(6), 1–10 (2016)

    Article  Google Scholar 

  8. Lv, H.; Feng, L.; Yang, A., et al.: High accuracy VLC indoor positioning system with differential detection[J]. IEEE Photon. J. 9(3), 1–13 (2017)

    Google Scholar 

  9. Yu, X.H.; Wang, J.P.; Lu, H.M.: Single LED based indoor positioning system using multiple photodetectors[J]. IEEE Photon. J. 1(1), 1–8 (2018)

    Google Scholar 

  10. Ming Xu, Weiwei Xia, Ziyan Jia, et al. A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor[C]. IEEE 2017 IEEE 85th Vehicular Technology Conference 1–5(2017).

  11. Lixuan Wang,Caili Guo. Indoor Visible Light Localization Algorithm with Multi-Directional PD Array[C]. Globecom Workshops. 1–6(2018).

  12. Hou, Y.; Xiao, S.; Bi, M., et al.: Single LED beacon-based 3-D indoor positioning using off-the-shelf devices[J]. IEEE Photon. J. 8(6), 1–11 (2016)

    Article  Google Scholar 

  13. Lim, J.: Ubiquitous 3D positioning systems by led-based visible light communications[J]. IEEE Wirel. Commun. 22(2), 80–85 (2015)

    Article  Google Scholar 

  14. Leopoldo A., et al., Metaheuristics and Optimization in Computer and Electrical Engineering[M].Lecture Notes in Electrical Engineering,2020.

  15. Sourav D., et al. Recent Advances in Hybrid Metaheuristics for Data Clustering[M].Wiley Publishing,2020.

  16. Wei, G.; Razmjooy, N.: A new optimisation algorithm based on OCM and PCM solution through energy reserve[J]. Int. J. Ambient Energy 1, 1–14 (2020)

    Google Scholar 

  17. Razmjooy, N.; Estrela, V.V.; Loschi, H.J.: Entropy-based breast cancer detection in digital mammograms using world cup optimization algorithm[J]. Int J. Swarm Intell. Res. 11(3), 1–18 (2020)

    Article  Google Scholar 

  18. Zhu, L.; Zhang, C.; Zhang, C., et al.: An improved theoretical nonelectric water saturation method for organic shale reservoirs[J]. IEEE Access 7(99), 51441–51457 (2019)

    Article  Google Scholar 

  19. Guan, W.P.; Wu, Y.X.; Xie, C.Y., et al.: High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms[J]. Opt. Eng. 56(10), 1–15 (2017)

    Article  Google Scholar 

  20. Peng, Q.; Guan, W.; Wu, Y., et al.: Three-dimensional high-precision indoor positioning strategy using Tabu search based on visible light communication[J]. Opt. Eng. 57(1), 1–11 (2018)

    Google Scholar 

  21. Chen, H.; Guan, W.P.; Li, S.M., et al.: Indoor high precision three-dimensional positioning system based on visible light communication using modified genetic algorithm[J]. Optics Commun. 413, 103–120 (2018)

    Article  Google Scholar 

  22. Wu, Y.X.; Liu, X.W.; Guan, W.P., et al.: High-speed 3D indoor localization system based on visible light communication using differential evolution algorithm[J]. Optics Commun. 424, 177–189 (2018)

    Article  Google Scholar 

  23. Wu, Y.X.; Guo, Z.H.; Liu, X.W., et al.: High precision and high speed of three-dimensional indoor localization system based on visible light communication using improved bacterial colony chemotaxis algorithm[J]. Opt. Eng. 58(3), 1–13 (2019)

    Google Scholar 

  24. Wu X.B., Wen S.S., Hua J., High precision 3D positioning system design using visible light communication based on ant colony algorithm[J]. acta photonica sinica, 46(12): 1–14(2017).

  25. Huang, L.; Wang, P.; Liu, Z., et al.: Indoor three-dimensional high-precision positioning system with bat algorithm based on visible light communication[J]. Appl Opt 58(9), 2226–2234 (2019)

    Article  Google Scholar 

  26. Kim, H.S.; Kim, D.; R, Yang S. H., , et al.: An indoor visible light communication positioning system using a rf carrier allocation technique[J]. J. Lightwave Technol. 31(1), 134–144 (2013)

    Article  Google Scholar 

  27. Keskin M. F., Sezer A. D., Gezici S., Localization via Visible Light Systems[C]. Proceedings of the IEEE. 1–26(2018).

  28. Li N., Qiao Y., Zhang T., et al. (2018). Dead-zone-free three-dimensional indoor positioning method based on visible light communication with dimensionality reduction algorithm[J]. Optical engineering, 57(3): 036114.1–036114.8.

  29. Yang X. S.,Deb S., Cuckoo Search via Lévy flights[C]. World Congress on Nature & Biologically Inspired Computing. 210–214(2009).

  30. Haruna, C.; Tutut, H.; Iztok, F., et al.: Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm[J]. Appl. Soft Comput. 61, 149–173 (2017)

    Article  Google Scholar 

  31. Aziz, M.A.E.; Hassanien, A.E.: Modified cuckoo search algorithm with rough sets for feature selection[J]. Neural Comput. Appl. 29(1), 925–934 (2016)

    Google Scholar 

  32. Huang, L.; Ding, S.; Yu, S.H., et al.: Chaos-enhanced Cuckoo search optimization algorithms for global optimization[J]. Appl. Math. Model. 40(5), 3860–3875 (2016)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We are very grateful to the Robotics Research Laboratory of Shandong University of Science and Technology for providing an experimental site for our research.

Funding

National Young Natural Science Foundation (NO.61702375, NO.61803235) of China, Key Research Programs of Shandong Province (NO. 2018GGX103011), Key Science and Technology Innovation Programs in Shandong Province (NO.2017CXGC0919), Youth Program of West Anhui University(NO.WXZQ1417, WXZR201903).

Author information

Authors and Affiliations

  1. Robot Research Center, Shandong University of Science and Technology, Qingdao, China

    Jia Chaochuan & Wang Chuanjiang

  2. College of Electronics and Information Engineering, West Anhui University, Lu’an, China

    Jia Chaochuan & Yang Ting

  3. Qingdao Engineering Vocational College, Qingdao, China

    Sun Mengli

Authors
  1. Jia Chaochuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Ting
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wang Chuanjiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sun Mengli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wang Chuanjiang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaochuan, J., Ting, Y., Chuanjiang, W. et al. High-Accuracy 3D Indoor Visible Light Positioning Method Based on the Improved Adaptive Cuckoo Search Algorithm. Arab J Sci Eng 47, 2479–2498 (2022). https://doi.org/10.1007/s13369-021-06144-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06144-y

Keywords

Search

Navigation