Design of the Model for Indoor Location Prediction Using IMU of Smartphone Based on Beacon

  • Jae-Gwang LeeEmail author
  • Seoung-Hyeon Lee
  • Jae-Kwang Lee
Part of the Studies in Computational Intelligence book series (SCI, volume 789)


IPS (Indoor positioning system) is a system that measures the user’s position in the room. Since IPS can’t use GPS (Global Positioning System), various researches are under way focusing on indoor location accuracy. IPS may also be unable to measure indoors because of signal loss, blind spots, etc. To solve this problem, Beacon’s RSSI signal is linearized using BITON algorithm and Kalman filter is applied. In addition, the position is predicted even when the signal is lost by measuring the instantaneous direction and the moving distance using the sensor of the smartphone. Therefore, in this paper, we propose a room location prediction model that can improve user’s position accuracy and detect user’s position in case of signal loss using Beacon and smartphone sensor.


Beacon Kalman filter Geomagnetic sensor IPS Indoor location prediction 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03036130).


  1. 1.
    Salas, A.C.: Indoor Positioning System based on Blutetooth low Energy. Bachelor Thesis, Universitat Politecnia de Catalunia Barcelonatech, Barcelona (2014, June)Google Scholar
  2. 2.
    Zhu, J., Luo, H., Li, Z.: RSSI based bluetooth low energy indoor positioning. In: 2014 International Conference on Indoor Positioning and Indoor Navigation (2014, October)Google Scholar
  3. 3.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’00), vol. 2, pp. 775–784 (2000, March)Google Scholar
  4. 4.
    Fang, L., Antsaklis, P.J., Montestruque, L.A., McMickell, M.B., Lemmon, M., Sun, Y., Fang, H., Koutroulis, I., Haenggi, M., Xie, M., Xie, X.: Design of a wireless assisted pedestrian dead reckoning system—the NavMote experience. IEEE Trans. Instrum. Measur. 54(6), 2342–2358 (2005)CrossRefGoogle Scholar
  5. 5.
    He, S., Chan, S. H. G., Yu, L., Liu, N.: Fusing noisy fingerprints with distance bounds for indoor localization. In: 2015 IEEE Conference on Computer Communications (INFOCOM), IEEE (2015)Google Scholar
  6. 6.
    Rai, A., Chintalapudi, K.K., Padmanabhan, V.N. Sen, R.: Zee: zero-effort crowd sourcing for indoor localization. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, ACM (2012)Google Scholar
  7. 7.
    Salas, A.C.: Indoor positioning system based on bluetooth low energy. A Degree’s Thesis Submitted to the Faculty of the Escola Tècnica d’Enginyeria de Telecomunicació de Barcelona Universitat Politècnica de Catalunya (2014, June)Google Scholar
  8. 8.
    Tao, Y., Papadias D., Sun, J.: The TPR*-tree: an optimized spatiotemporal access method for predictive queries. In: Proceedings of the 29th International Conference on Very Large Data Bases, pp. 790–801 (2003)CrossRefGoogle Scholar
  9. 9.
    Tao, Y., Faloutsos, C., Papadias D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In Proceedings of the 10th ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp. 611–622 (2004)Google Scholar
  10. 10.
    He, S., Gary Chan, S.-H.: Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun. Surv. Tutorials (2015)Google Scholar
  11. 11.
    Rainer, M.: Indoor positioning technologies. Ph.D. Dissertation, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland (2012)Google Scholar
  12. 12.
    Li, X., Wang, J., Liu, C.: A Bluetooth/PDR integration algorithm for an indoor positioning system. Sensors 15(10), 24862–24885 (2015)CrossRefGoogle Scholar
  13. 13.
    Li, Z., Liu, C., Gao, J., Li, X.: An improved WiFi/PDR integrated system using an adaptive and robust filter for indoor localization. Int. Soc. Photogrammetry Remote Sens. Int. J. GeoInformation 5(12), 224–239 (2016)Google Scholar
  14. 14.
    Cai, Y.D., Clutter, D., Pape, G., Han, J., Welge, M., Auvil, L.: MAIDS: Mining alarming incidents from data streams. In: SIGMOD 2004. Paris, France (2004, June)Google Scholar
  15. 15.
    Ali-Loytty, S., Perala, T., Honkavirta, V., Piche, R.: Fingerprint Kalman filter in indoor positioning applications. In: 3rd IEEE Multi-conference on Systems and Control (2009, July)Google Scholar
  16. 16.
    Fukuju, Y., Minami, M., Morikawa, H., Aoyama, T., DOLPHIN: an autonomous indoor positioning system in ubiquitous computing environment. In: Conference: Conference: Software Technologies for Future Embedded Systems (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jae-Gwang Lee
    • 1
    Email author
  • Seoung-Hyeon Lee
    • 2
  • Jae-Kwang Lee
    • 1
  1. 1.Department of Computer Engineering Hannam UniversityDaejeonKorea
  2. 2.Information Security Research DivisionETRIDaejeonKorea

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