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access icon openaccess Indoor positioning technology based on map information perception

With the increasing demand for location-based services, indoor positioning technology has become one of the most attractive areas of research. Microelectromechanical systems sensors in smart terminals are used to realise a pedestrian dead reckoning algorithm. Owing to the accumulated error increased with time, the results of positioning will produce a large error, and an indoor positioning method based on the perception and constraint of map information is designed including straight path constraint and inflection point constraint. In the method of inflection point constraint, several common machine learning algorithms are compared through the experiments, and the secondary discriminant method is utilised to detect the inflection point with a detection accuracy of 97.62%. Finally, the performances of the improved algorithm and the traditional dead reckoning algorithm are compared in the experiments. The results show that the average positioning accuracy of the improved algorithm is 0.073 m, the positioning accuracy within 1 m reaches 100%, it is obviously higher than that of the traditional positioning algorithm and the effectiveness of the algorithm is verified.

References

    1. 1)
      • 11. Wang, X., Gao, L., Mao, S., et al: ‘Deepfi: deep learning for indoor fingerprinting using channel state information’. Proc. IEEE Wireless Communications and Networking Conf. (WCNC 2015), New Orlean, LA, March 2015, pp. 16661671.
    2. 2)
      • 16. Singh, J.M., Narayanan, P.J.: ‘Real-time ray tracing of implicit surfaces on the GPU’, IEEE Trans. Vis. Comput. Graph., 2010, 16, (2), pp. 261272.
    3. 3)
      • 2. Wang, X., Mao, S., Pandey, S., et al: ‘CA2T: cooperative antenna arrays technique for pinpoint indoor localization’. Proc. MobiSPC 2014, Niagara Falls, Canada, August 2014, pp. 392399.
    4. 4)
      • 10. Wu, Z., Li, C., Ng, J., et al: ‘Location estimation via support vector regression’, IEEE Trans. Mob. Comput., 2007, 6, (3), pp. 311321.
    5. 5)
      • 18. Ye, W., Wenjun, L., Hongbo, Z.: ‘Experimental study on indoor channel model for wireless sensor networks and internet of things’. IEEE Int. Conf. Companion Technology (ICCT), Nanjin, China, November 2010, pp. 624627.
    6. 6)
      • 4. Youssef, M.A., Agrawala, A., Shankar, A.U.: ‘WLAN location determination via clustering and probability distributions’. IEEE Int. Conf. Pervasive Computing and Communications, Fort Worth, USA, 2003, pp. 143150.
    7. 7)
      • 1. Liu, H., Darabi, H., Banerjee, P., et al: ‘Survey of wireless indoor positioning techniques and systems’, IEEE Trans. Syst. Man Cybern. C, 2007, 37, (6), pp. 10671080.
    8. 8)
      • 6. Prasithsangaree, P., Krishnamurthy, P., Chrysanthis, P.K.: ‘On indoor position location with wireless LANs’. Proc. IEEE Personal, Indoor and Mobile Radio Communications (PIMRC), Lisbon, Portugal, September 2002.
    9. 9)
      • 3. Ma, J., Li, X., Tao, X., et al: ‘Cluster filtered KNN: a WLAN-based indoor positioning scheme’. Int. Symp. World of Wireless, Mobile and Multimedia Networks, Newport Beach, USA, June 2008, pp. 18.
    10. 10)
      • 14. Dama, Y.: ‘MIMO indoor propagation prediction using 3D shooting and bouncing ray tracing technique for 2.4 GHz and 5 GHz’. Proc. Fifth European Conf. Antennas and Propagation, Rome, Italy, April 2011, pp. 16551658.
    11. 11)
      • 13. Dama, Y.: ‘Indoor channel measurement and prediction for 802.11n system’. IEEE Conf. Vehicular Technology, San Francisco, USA, September 2011, pp. 15.
    12. 12)
      • 9. Liu, H., Gan, Y., Yang, J., et al: ‘Push the limit of WiFi based localization for smartphones’. Proc. ACM Mobicom'12, Istanbul, Turkey, August 2012, pp. 305316.
    13. 13)
      • 5. Liu, K., Liu, X., Li, X.: ‘Guoguo: enabling fine-grained indoor localization via smartphone’. Proc. ACM MobiSys'13, Taipei, Taiwan, 2013.
    14. 14)
      • 17. Irfan, A, Sara, O, Tamer, K., et al: ‘Characterization of the indoor–outdoor radio propagation channel at 2.4 GHz’. GCC Conf. and Exhibition, Dubai, UAE, February 2011, pp. 605608.
    15. 15)
      • 8. Fadib, F., Katabi, D.: ‘Seeing through walls using WiFi!’. Proc. ACM Networked Systems Design and Implementation (NSDI'13), Lombard, IL, April 2013, pp. 7586.
    16. 16)
      • 15. Saeidi, C., Fard, A., Hodjatkashani, F.: ‘Full three-dimensional radio wave propagation prediction model’, IEEE Trans. Antennas Propag., 2012, 60, (5), pp. 24622471.
    17. 17)
      • 12. Wang, X., Gao, L., Mao, S.: ‘Phasefi: phase fingerprinting for indoor localization with a deep learning approach’. Proc. IEEE The Global Communications Conf. (GLOBECOM 2015), San Diego, CA, December 2015.
    18. 18)
      • 7. Xiao, J., Wu, K., Yi, Y., et al: ‘FIFS: fine-grained indoor fingerprinting system’. Proc. IEEE Int. Conf. Computer Communications and Networks (ICCCN), Munich, Germany, 12 August 2012, pp. 17.
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