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An indoor localization solution using Bluetooth RSSI and multiple sensors on a smartphone

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Abstract

In this paper, we propose an indoor positioning system using a Bluetooth receiver, an accelerometer, a magnetic field sensor, and a barometer on a smartphone. The Bluetooth receiver is used to estimate distances from beacons. The accelerometer and magnetic field sensor are used to trace the movement of moving people in the given space. The horizontal location of the person is determined by received signal strength indications (RSSIs) and the traced movement. The barometer is used to measure the vertical position where a person is located. By combining RSSIs, the traced movement, and the vertical position, the proposed system estimates the indoor position of moving people. In experiments, the proposed approach showed excellent performance in localization with an overall error of 4.8%.

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References

  1. Welcome to GPS.gov. Available at: http://www.gps.gov/. Accessed 21 Apr 2016

  2. The Qualcomm/SnapTrack Wireless-Assisted GPS Hybrid Positioning System and Results from Initial Commercial Deployments,. Available at: https://www.ion.org/publications/abstract.cfm?articleID=2037. Accessed 21 Apr 2016

  3. Glonass. Available at: http://www.glonass.it/eng/. Accessed 21 Apr 2016

  4. Galileo | European GNSS Agency. Available at: http://www.gsa.europa.eu/galileo. Accessed 21 Apr 2016

  5. BeiDou Navigation Satellite System — Home. Available at: http://en.beidou.gov.cn/. Accessed 21 Apr 2016

  6. Müller P, Raitoharju M, Piché R (2014) A field test of parametric WLAN-fingerprint-positioning methods. In: 2014 17th International Conference on information FUSION (FUSION), pp 1–8

  7. Loeffler A, Wissendheit U, Gerhaeuser H, Kuznetsova D (2010) A multi-purpose RFID reader supporting indoor navigation systems. In: 2010 I.E. International Conference on RFID-Technology and applications (RFID-TA) 43–48. doi:10.1109/RFID-TA.2010.5529857

  8. Fischer G, Dietrich B, Winkler F (2004) Bluetooth indoor localization system. In: proceedings of the 1st workshop on positioning, navigation and communication

  9. Friis HT (1946) A note on a simple transmission formula. Proc IRE 34:254–256

    Article  Google Scholar 

  10. Chan S, Sohn G (2012) Indoor localization using wi-fi based fingerprinting and trilateration techiques for lbs applications. ISPRS - International archives of the photogrammetry. Remote Sens Spat Inf Sci XXXVIII-4(C26):1–5

    Google Scholar 

  11. Quan M, Navarro E, Peuker B (2010) Wi-fi localization using RSSI fingerprinting. Comput Eng

    Google Scholar 

  12. Li WW-L, Iltis RA (2013) A smartphone localization algorithm using RSSI and inertial sensor measurement fusion. 3335–3340. doi:10.1109/GLOCOM.2013.6831587

  13. Lanzisera S, Zats D, Pister KSJ (2011) Radio frequency time-of-flight distance measurement for low-cost Wireless sensor localization. IEEE Sensors J 11:837–845

    Article  Google Scholar 

  14. Barshan B, Ayrulu B (1998) Performance comparison of four time-of-flight estimation methods for sonar signals. Electron Lett 34:1616–1617

    Article  Google Scholar 

  15. Ojeda L, Borenstein J (2007) Personal dead-reckoning system for GPS-denied environments. In: 2007 I.E. International workshop on safety, security and rescue robotics 1–6. doi:10.1109/SSRR.2007.4381271

  16. Shahidi S, Valaee S (2015) GIPSy: geomagnetic indoor positioning system for smartphones. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN) 1–7. doi:10.1109/IPIN.2015.7346761

  17. Nakazawa Y, Makino H, Nishimori K, Wakatsuki D, Komagata H (2013) Indoor positioning using a high-speed, fish-eye lens-equipped camera in visible light communication. In: 2013 International Conference on indoor positioning and indoor navigation (IPIN) 1–8. doi:10.1109/IPIN.2013.6817855

  18. Jayatilleke L, Zhang N (2013) Landmark-based localization for unmanned aerial vehicles. In: systems Conference (SysCon), 2013 I.E. International 448–451. doi:10.1109/SysCon.2013.6549921

  19. Liang JZ et al (2013) Image based localization in indoor environments.Computing for Geospatial Research and Application (COM. Geo), 2013 Fourth International Conference on. IEEE

  20. McKenna SJ, Gong S (1997) Non-intrusive person authentication for access control by visual tracking and face recognition. International Conference on audio-and video-based biometric person authentication. Springer, Berlin

    Google Scholar 

  21. Clarke R (2001) Person location and person tracking-technologies, risks and policy implications. Inf Technol People 14(2):206–231

    Article  Google Scholar 

  22. Bohonos S, Lee A, Malik A, Thai C, Manduchi R (2007) Universal real-time navigational assistance (URNA): an urban bluetooth beacon for the blind. ACM Press, p 83. doi:10.1145/1248054.1248080

  23. iBeacon for Developers - Apple Developer. Available at: https://developer.apple.com/ibeacon/. Accessed 21 Apr 2016

  24. Viani F, Rocca P, Benedetti M, Oliveri G, Massa A (2010) Electromagnetic passive localization and tracking of moving targets in a wsn-infrastructured environment. Inverse Prob 26(7):074–003. doi:10.1088/0266-5611/26/7/074003

    Article  MATH  Google Scholar 

  25. Faulkner W, Alwood R, Taylor D, Bohlin J (2010) GPS-denied pedestrian tracking in indoor environments using an imu and magnetic compass. Proceedings of the 2010 International technical meeting of the Institute of Navigation (ITM), pp 198-204

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Acknowledgements

This work was supported by the Soonchunhyang University Research Fund and also supported by a grant (NRF-2015M3A9D7067388) of the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP), Republic of Korea

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Soonchunhyang University, Asan, 336-745, South Korea

    Keonsoo Lee & Yunyoung Nam

  2. Department of Medical IT Engineering, Soonchunhyang University, Asan, 336-745, South Korea

    Se Dong Min

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  1. Keonsoo Lee
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  2. Yunyoung Nam
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  3. Se Dong Min
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Correspondence to Yunyoung Nam.

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Lee, K., Nam, Y. & Min, S.D. An indoor localization solution using Bluetooth RSSI and multiple sensors on a smartphone. Multimed Tools Appl 77, 12635–12654 (2018). https://doi.org/10.1007/s11042-017-4908-2

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  • DOI: https://doi.org/10.1007/s11042-017-4908-2

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