KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Enhancing Location Estimation and Reducing Computation using Adaptive Zone Based K-NNSS Algorithm

  • Published : 2009.02.23
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Abstract

The purpose of this research is to accurately estimate the location of a device using the received signal strength indicator (RSSI) of IEEE 802.11 WLAN for location tracking in indoor environments. For the location estimation method, we adopted the calibration model. By applying the Adaptive Zone Based K-NNSS (AZ-NNSS) algorithm, which considers the velocity of devices, this paper presents a 9% improvement of accuracy compared to the existing K-NNSS-based research, with 37% of the K-NNSS computation load. The accuracy is further enhanced by using a Kalman filter; the improvement was about 24%. This research also shows the level of accuracy that can be achieved by replacing a subset of the calibration data with values computed by a numerical equation, and suggests a reasonable number of calibration points. In addition, we use both the mean error distance (MED) and hit ratio to evaluate the accuracy of location estimation, while avoiding a biased comparison.

Keywords

References

  1. N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, “The cricket location support system,” MOBICOM 2000, pp. 32-43, Aug. 2000.
  2. R. Want, A. Hopper, V. Falco, and J. Gibbons, “The active badge location system,” ACM Transactions on Information Systems, vol. 10, no. 1, pp. 91-102, Jan. 1992. https://doi.org/10.1145/128756.128759
  3. T. Roos, P. MyllymaÈ ki, and H. Tirri, “A statistical modeling approach to location estimation,” IEEE Transactions on Mobile Computing, vol. 1, no. 1, Jan.-Mar. 2002.
  4. T. S. Stamoulakatos and E. D. Sykas, “Hidden Markov modeling and macroscopic traffic filtering supporting location-based services,” Wireless Communications and Mobile Computing, vol. 7, no. 4, pp:415-429, May 2007. https://doi.org/10.1002/wcm.350
  5. M. Ito, S. Tsujimichi, and Y. Kosuge, “Tracking a three-dimensional moving target with distributed passive sensors using extended Kalman filter,” Electronics and Communications in Japan (Part I: Communications), vol. 84, no. 7, pp: 74-85, July 2001. https://doi.org/10.1002/ecja.1026
  6. Kun Yang, Chris Todd, Jie Li, Nektarios Georgalas and Manooch Azmoodeh, "A location-based service advertisement algorithm for pervasive service discovery in wireless mobile networks," Wireless Communications and Mobile Computing, Early View, 2008.
  7. P. Bahl, A. Balachandran and V.N. Padmanabhan, “Enhancements to the RADAR user location and tracking System,” Microsoft Research Technical Report, Feb. 2008.
  8. P. Bahl and V.N. Padmanabhan, “RADAR: An in-building RF-based location and tracking system,” INFOCOM 2000 19th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775-784, Mar. 2000.
  9. L. A. Castro and J. Favela, “Continuous tracking of user location in wlans using recurrent neural networks,” Sixth Mexican International Conference on Computer Science, pp. 174-181, 2005.
  10. X. Chai and Q. Yang, “Reducing the calibration effort for location estimation using unlabeled samples,” IEEE International Conference on Pervasive Computing and Communications (PerCom2005), 2005.
  11. T.Tsai, C.Li, and T. Lin, “Reducing calibration effort for wlan location and tracking system using segment technique,” IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06), vol. 2, pp. 46-51, 2006.
  12. M. Ciurana, F. Barceló, and S. Cugno, “Indoor Tracking in WLAN Location with TOA Measurements,” Fourth ACM International Workshop on Mobility Management and Wireless Access, pp. 121-125, 2006.
  13. I. Guvenc, “Enhancements to RSS based Indoor Tracking Systems using Kalman filters,” MS thesis, 2001.
  14. John Platt and John Krumm, “Minimizing calibration effort for an indoor 802.11 device location measurement system,” Microsoft Research Technical Report, Nov. 2003.
  15. Josep Vidal, Montse Nájar, and René Játiva, “High resolution time-of-arrival detection for wireless positioning systems,” IEEE Vehicular Technology Conference, vol. 4, pp. 2283-2287, 2002.
  16. L. R. Rabiner and B. H. Juang, “An introduction to hidden markov models,” IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, Jan. 1986.
  17. R. O. Duda , P. E. Hart, and D. G. Stork, “Pattern Classification Second Edition,” John Wiley & Son, Inc., 2001.
  18. I. Guvenc, C. T. Abdallah, R. Jordan, and O. Dedeoglu, “Enhancements to RSS based indoor tracking systems using Kalman filters,” International Signal Processing Conference and Global Signal Processing Expo, Mar.-Apr. 2003.
  19. V. Abhijit, C. Ellis, and X. Fan, “Experiences with an inbuilding location tracking system: Uhuru,” IEEE Personal, Indoor and Mobile Radio Communications, vol. 3, pp. 2789-2793, Sept. 2003.
  20. R. Kalman, “A new approach to linear filtering and prediction problems,” Transactions on ASME-Journal of Basic Engineering, vol. 82 (Series D), pp. 35-45, 1960.
  21. P. Maybeck, “Stochastic Models, Estimation, and Control Volume 1,” Academic Press, pp. 442, 1979.
  22. D. Simon, “Kalman filtering, embedded systems programming,” Embedded.com article, June 2001.
  23. G. Welch and G. Bishop, "An introduction to the Kalman filter," Technical Report TR 95-041, http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html, Feb 2008.
  24. Microsoft, http://msdn2.microsoft.com/en-us/library/ms892542.aspx, 2006.
  25. T. Roos, P. Myllyma¨ki, H. Tirri, P. Misikangas, and J. Sieva¨nen, “A probabilistic approach to WLAN user location estimation,” International Journal of Wireless Information Networks, vol. 9, no. 3, pp. 155-164, 2002. https://doi.org/10.1023/A:1016003126882