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Target Localization with Visible Light Communication in High Ambient Light Environments

  • Kofi NyarkoEmail author
  • Emmanuel Shedu
Conference paper
  • 72 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)

Abstract

This paper builds on prior research that demonstrated how inexpensive commercial off-the-shelf lighting components and microcontrollers could be used to construct a solution for occupant and asset localization and tracking through visible light communication (VLC). A significant challenge encountered in the prior work was mitigating the effect of ambient light optical interference. This paper describes the implementation of several techniques to negate the effect of ambient light interference under varying conditions. The techniques involved modifications to the modulation scheme employed, redundant transmissions over multiple wavelengths, and an adaptive digital filtering technique. Furthermore, this paper discusses the challenges involved with implementation the approach on a severely resource constrained microcontroller and the optimization strategies employed. The overall effectiveness of the system is measured and discussed.

Keywords

Visible light communication Indoor localization Indoor position estimation Ambient light compensation 

References

  1. 1.
    How Much Energy is Used for Lighting. EIA. http://www.eia.gov/tools/faqs/faq.cfm?id=99&t=3
  2. 2.
    Nyarko, K., Emiyah, C., Mbugua, S.: Building occupant and asset localization and tracking using visible light communication. In: Proceedings of the SPIE, Automatic Target Recognition XXVI, vol. 9844, pp. 98440B, 12 May 2016.  https://doi.org/10.1117/12.2224089
  3. 3.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the IEEE INFOCOM 2000, March, vol. 2, pp. 775–784 (2000)Google Scholar
  4. 4.
    Yang, Y., Chen, X., Lin, Z., Liu, B., Chen, H.D.: Design of indoor wireless communication system using LEDs. In: Communications and Photonics Conference and Exhibition (ACP), Asia, vol. 2009, pp. 1–2, 2–6 November 2009Google Scholar
  5. 5.
    Yánez, V.G., Torres, J.R., Alonso, J.B., Borges, J.A.R., Sánchez, C.Q., González, C.T., Jiménez, R.P., Rajo, F.D.: Illumination interference reduction system for VLC Communications. In: Proceedings of the WSEAS International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems, pp. 252–257 (2009)Google Scholar
  6. 6.
    Liu, Y.F., Yeh, C.H., Wang, Y.C., Chow, C.W.: Employing NRZI code for reducing background noise in LED visible light communication. In: 18th OptoElectronics and Communications Conference Held Jointly with International Conference on Photonics in Switching (2013)Google Scholar
  7. 7.
    Gour, S., Murarka, S., Kumar, S.: Review on reduction of optical background noise in light-emitting diode (LED) optical wireless communication systems using Hadamard error correcting code. Intern. J. Emerg. Technol. Adv. Eng. 4(9), 233–235 (2014). ISSN 2250-2459, ISO 9001:2008 Certified JournalGoogle Scholar
  8. 8.
    Yang, S.-H., Kim, H.-S., Son, Y.-H., Han, S.-K.: Reduction of optical interference by wavelength filtering in RGB-LED based indoor VLC system. In: Proceedings of the 16th Opto-Electronics and Communication Conference, OECC, pp. 551–552, July 2011Google Scholar
  9. 9.
    Rufo, J., Quintana, C., Delgado, F., Rabadan, J., Perez-Jimenez, R.: Considerations on modulations and protocols suitable for visible light communications (VLC) channels: low and medium baud rate indoor visible light communications links. In: 2011 IEEE Consumer Communications and Networking Conference (CCNC), pp. 362–364, 9–12 January 2011Google Scholar
  10. 10.
    Lo, B.P.L., Velastin, S.A.: Automatic congestion detection system for underground platforms. In: International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 158–161 (2000)Google Scholar
  11. 11.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadows in video streams. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1337–1342 (2003)CrossRefGoogle Scholar
  12. 12.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  13. 13.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture modelsfor real-time tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 246–252 (1999)Google Scholar
  14. 14.
    Dong, W.T., Soh, Y.S.: Image-based fraud detection in automatic teller machine. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 6(11), 13–18 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Morgan State UniversityBaltimoreUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA

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