Modeling the Effect of Human Body on TOA Based Indoor Human Tracking

  • Yishuang Geng
  • Jie He
  • Kaveh Pahlavan


In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging.


TOA Body area network Ranging error Human tracking Indoor localization Indoor location system 



The authors would like to thank Mao Wenbo from Wake Forest University and Adria Fung from WPI for editing the paper and Dr. Yunxing Ye from CWINS, WPI for building the measurement system. The technical discussion with Dr. Yadong Wan from USTB is of great help. This work has been performed under the American Recovery and Reinvestment Act Measurement, Science and Engineering Grants Program (NIST Grant No. 60NANB10D001), which is supported by the National Institute of Standards and Technology (NIST). This work is partly supported by the National Natural Science Foundation of China (Grant Nos. 61003251 and 61172049) and Doctoral Fund of Ministry of Education of China (Grant No. 20100006110015).


  1. 1.
    N. Moayeri, J. Mapar, S. Tompkins, and K. Pahlavan, Emerging opportunities for localization and tracking, Special issue on navigation using signals of opportunity, IEEE Wireless Magazine, Vol. 18, No. 4, pp. 8–9, April 2011.Google Scholar
  2. 2.
    K. Pahlavan, X. Li, and J. P. Makela, Indoor geolocation science and technology, IEEE Communications Magazine, Vol. 40, pp. 112–118, 2002.CrossRefGoogle Scholar
  3. 3.
    J. He, Y. Geng, and K. Pahlavan, Modeling indoor TOA ranging error for body mounted sensors, In 2012 IEEE 23nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, September 2012.Google Scholar
  4. 4.
    J. He, S. Li, K. Pahlavan, and Q. Wang, A realtime testbed for performance evaluation of indoor TOA location system, In IEEE International Conference on Communications (ICC), Ottawa, Canada, June 2012.Google Scholar
  5. 5.
    X. Zheng, and G. Bao, The performance of simulated annealing algorithms for Wi-Fi localization using Google indoor map, In IEEE 76th Vehicular Technology Conference (VTC), Quebec City, Canada, September 2012.Google Scholar
  6. 6.
    E, Andrea, X. Chen, Y. Li, and R. G. Micheal, RSS-based node localization in the presence of attenuating objects, In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011.Google Scholar
  7. 7.
    C. Y. Park, H. Cho, D. H. Park, S. E. Cho, and J. W. Park, AOA localization system design and implementation based on zigbee for applying greenhouse, In 2010 5th IEEE International Conference on Embedded and Multimedia Computing (EMC), August 2010.Google Scholar
  8. 8.
    J. Lee, and R. Scholtz, Ranging in a dense multipath environment using an UWB radio link, IEEE Journal on Selected Areas in Communications, Vol. 20, No. 9, pp. 1677–1683, 2002.CrossRefGoogle Scholar
  9. 9.
    D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, Ranging with ultrawide bandwidth signals in multipath environments, In Proceedings of IEEE, Special Issue on UWB Technology and Emerging Applications, February 2009.Google Scholar
  10. 10.
    M. Heidari, F. O. Akgul, and K. Pahlavan, Identification of the absence of direct path in indoor localization systems, International Journal of Wireless Information Networks, Vol. 15, No. 3–4, pp. 117–127, 2008.CrossRefGoogle Scholar
  11. 11.
    K. Pahlavan, Y. Ye, R. Fu, and U. Khan, Challenges in channel measurement and modeling for RF localization inside the human body, 2012 invited paper, special issue on ICL-GNSS best papers, International Journal of Embedded and Real-Time Communication Systems, Springer, 2012.Google Scholar
  12. 12.
    S. Pranay, K. Pahlavan, and U. Khan, Accuracy of localization system inside human body using a fast FDTD simulation technique, In Medical Information and Communication Technology (SMICT), San Diego, CA, 26–29 March 2012.Google Scholar
  13. 13.
    Y. Geng, J. He, H. Deng, and K. Pahlavan, Modeling the effect of human body on TOA ranging for indoor human tracking with wrist mounted sensor, In 16th International Symposium on Wireless Personal Multimedia Communications (WPMC), Atlantic City, NJ, June 2013.Google Scholar
  14. 14.
    IEEE 802.15 TG6, Draft of Channel Model for Body Area Network, November 2010.Google Scholar
  15. 15.
    S. Li, J. He, R. Fu, and K. Pahlavan, A hardware platform for performance evaluation of in-body sensors, In 6th IEEE International Symposium on Medical Information and Communication Technology (ISMICT), San Diego, CA, 26–29 March 2012.Google Scholar
  16. 16.
    Y. Geng, J. Chen, and K. Pahlavan, Motion detection using RF signals for the first responder in emergency operations, In Proceedings of the 24nd Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, September 2013.Google Scholar
  17. 17.
    F. Della Rosa, L. Xu, J. Nurmi, M. Pelosi, C. Laoudias, and A. Terrezza, Hand-grip and body-loss impact on RSS measurements for localization of mass market devices, In International Conference on Localization and GNSS (ICL-GNSS), pp. 58–63, 2011.Google Scholar
  18. 18.
    N. Alsindi, B. Alavi, and K. Pahlavan, Measurement and modeling of ultrawideband TOA-based ranging in indoor multipath environments, IEEE Transactions on Vehicular Technology, Vol. 58, No. 3, pp. 1046–1058, 2009.CrossRefGoogle Scholar
  19. 19.
    B. Alavi, and K. Pahlavan, Modeling of the TOA based distance measurement error using UWB indoor radio measurements, IEEE Communication Letters, Vol. 10, No. 4, pp, 275–277, 2006.CrossRefGoogle Scholar
  20. 20.
    S. Thuraiappah, H. David, and H. Mark, WASP: a system and algorithms for accurate radio localization using low-cost hardware, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 41, No. 2, pp. 211–222, 2011.CrossRefGoogle Scholar
  21. 21.
    M. Garardine, and V. Prithiviraj, UWB localization techniques for precision automobile parking system, In IEEE 10th International Conference on Electromagnetic Interference and Compatibility (INCEMIC), pp. 621–626, November 2008.Google Scholar
  22. 22.
    Q. Wang, K. Masami, and J. Wang, Channel modeling and BER performance for wearable and implant UWB body area links on chest, In IEEE International Conference on Ultra-wide-band (ICUWB), Vancouver, Canada, September 2009, pp. 316–320.Google Scholar
  23. 23.
    J. Chen, Y. Ye, and K. Pahlavan, UWB characteristics of creeping wave for RF localization around the human body, In Proceedings of the 23nd Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Sydney, September 2012.Google Scholar
  24. 24.
    Y. Geng, Y. Wan, J. He, and K. Pahlavan, An empirical channel model for the effect of human body on ray tracing, In Proceedings of the 24nd Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, September 2013.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringWorcester Polytechnic InstituteWorcesterUSA
  2. 2.School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations