Wireless Networks

, Volume 22, Issue 5, pp 1751–1766 | Cite as

FISCP: fine-grained device-free positioning system for multiple targets working in sparse deployments

  • Binbin Xie
  • Dingyi Fang
  • Tianzhang Xing
  • Lichao Zhang
  • Xiaojiang ChenEmail author
  • Zhanyong Tang
  • Anwen Wang


Device-free localization has long been playing a key role in the anti-intrusion applications. However, current multi-target localization solutions mainly use uneconomical equipment, which are not cost-efficient for large-scale scenarios . Moreover, they only consider the line-of-sight (LoS) path signals distorted by the targets, hence can’t exactly pinpoint the locations when faced with multipath effects and non-line-of-sight (NLoS), which are typical in real-world deployments. In this paper, we propose FISCP, a fine-grained device-free positioning system for multiple targets working in sparse deployments. The RFID passive tags are employed, which are much cheaper than other devices for localization. Meanwhile, unlike past approaches, which ignore the multipath effects or even take multipath as detrimental, FISCP exploits the dynamic distortion in multipath caused by the targets and considers the distortion as fingerprints for localization. We make a prototype system for FISCP using the commercial off-the-shelf products, including RFID systems and omnidirectional antennas, and develop a software program for the RFID systems. All the experiments are conducted in the deployments where the distance interval between each pair of tags is 1.2 m, and the deployments are sparse with respect to the short communication range of passive RFID systems (from a few meters up to tens of meters). The results of our experiments demonstrate that FISCP is effective in multi-target localization with low localization errors of 0.33 m in average.


Fine-grained Device-free localization Multiple targets Multipath 



This work is supported by Project National Key Technology R&D Program 2013BAK01B02 and Project NSFC (61170218, 61272461, 61373177).


  1. 1.
    Adib, F., Kabelac, Z., Katabi, D., & Miller, R. C. (2014). 3d tracking via body radio reflections. In Proceedings of USENIX NSDI.Google Scholar
  2. 2.
    Adib, F., & Katabi, D. (2013). See through walls with wi-fi!. In Proceedings of ACM SigComm (pp. 75–86).Google Scholar
  3. 3.
    Bocca, M., Kaltiokallio, O., Patwari, N., & Venkatasubramanian, S. (2013). Multiple target tracking with RF sensor networks. IEEE Transactions on Mobile Computing, 13(8), 1787–1800.CrossRefGoogle Scholar
  4. 4.
    Chen, D., Cho, K.-T., Han, S., Jin, Z., & Shin, K. G. (2015). Invisible sensing of vehicle steering with smartphones. In Proceedings of ACM MobiSys (pp. 1–13).Google Scholar
  5. 5.
    Chen, Y., Liu, Z., Fu, X., & Liu, B. (2013). Theory underlying measurement of aoa with a rotating directional. In Proceedings of IEEE InfoCom (pp. 2490–2498).Google Scholar
  6. 6.
    Choi, J. S., Lee, H., Engels, D. W., & Elmasri, D. W. (2012). Passive UHF RFID based localization using detection of tag interference on smart shelf. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 42(2), 268–274.CrossRefGoogle Scholar
  7. 7.
    Fadel, A., Zachary, K., & Dina, K. (2015). Multi-person localization via rf body refections. In Proceedings of USENIX NSDI.Google Scholar
  8. 8.
    Gang, L. (2011). Bandwidth dependence of CW ranging to UHF RFID tags in severe multipath environments. In Proceedings of IEEE RFID (pp. 19–25).Google Scholar
  9. 9.
    Griffin, J., & Durgin, G. (2009). Complete link budgets for backscatter-radio and RFID systems. In Proceedings of IEEE Antennas and Propagation Magazine (pp. 11–25).Google Scholar
  10. 10.
    Han, J., Qian, C., Wang, X., Ma, D., Zhao, J., Zhang, P., Xi, W., & Jiang, Z. (2014). Twins: Device-free object tracking using passive tags. In Proceedings of IEEE InfoCom (pp. 469–476).Google Scholar
  11. 11.
    Hekimian-Williams, C. (2010). Accurate localization of rfid tags using phase difference. In Proceedings of IEEE RFID (pp. 89–96).Google Scholar
  12. 12.
    Jacobson, V. (1988). Congestion avoidance and control. In Proceedings of ACM SIGCOMM (pp. 314–329).Google Scholar
  13. 13.
    Joshi, K., Bharadia, D., Kotaru, M., & Katti, S. (2015). Wideo: Fine-grained device-free motion tracing using RF backscatter. In Proceedings of USENIX NSDI (pp. 189–204).Google Scholar
  14. 14.
    Li, L., Hu, P., Peng, C., Shen, G., & Zhao, F. (2014). Epsilon: A visible light based positioning system. In Proceedings of USENIX NSDI (pp. 331–343).Google Scholar
  15. 15.
    Liu, W., Wang, D., Jiang, H., Liu, W., & Wang, C. An approximate convex decomposition protocol for wireless sensor network localization in arbitrary-shaped fields. IEEE Transactions on Parallel and Distributed Systems, 26(12), 1853–1861.Google Scholar
  16. 16.
    Liu, Y., Chen, L., Pei, J., Chen, Q., & Zhao, Y. (2007). Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In Proceedings of IEEE Percom (pp. 37–46).Google Scholar
  17. 17.
    Liu, Y., Yang, Z., Wang, X., & Jian, L. (2010). Location, localization, and localizability. Journal of Computer Science and Technology, 25(2), 274–297.CrossRefGoogle Scholar
  18. 18.
    Luo, J., Zhang, J., Yu, L., & Wang, X. (2014). The role of location popularity in multicast mobile adhoc networks. IEEE Transactions on Wireless Communications, 14(4), 2131–2143.CrossRefGoogle Scholar
  19. 19.
    Ma, H., Zeng, C., & Ling, C. (2012). A reliable people counting system via multiple cameras. IEEE Transactions on Intelligent Systems and Technology, 3(2), 1–31.CrossRefGoogle Scholar
  20. 20.
    Miao, Q., Xu, P., Liu, T., Yang, Y., Zhang, J., & Li, W. (2013). Linear feature separation from topographic maps using energy density and shear transform. IEEE Transactions on Image Processing, 22(4), 1548–1558.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Nannuru, S., Li, Y., Zeng, Y., Coates, M., & Yang, B. (2013). Radio-frequency tomography for passive indoor multitarget tracking. IEEE Transactions on Mobile Computing, 12(12), 2322–2333.Google Scholar
  22. 22.
    Shangguan, L., Yang, Z., Liu, A.X., Zhou, Z., & Liu, Y. (2015). Relative localization of rfid tags using spatial-temporal phase profiling. In Proceedings of USENIX NSDI (pp. 251–263).Google Scholar
  23. 23.
    Sheng, Q. Z., Li, X., & Zeadally, S. (2008). Enabling next-generation RFID applications: solutions and challenges. Computer, 41(9), 21–28.Google Scholar
  24. 24.
    Tanenbaum, A. S., & Wetherall, D. J. (2011). Computer Networks (5th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  25. 25.
    Tapia, D.I., Alonso, R.S., Rodriguez, S., De La Prieta, F., Corchado, J.M., & Bajo, J. (2011). Implementing a real-time locating system based on wireless sensor networks and artificial neural networks to mitigate the multipath effect. In Proceedings of Information Fusion (FUSION) (pp. 1–8).Google Scholar
  26. 26.
    Thiagarajan, A., Ravindranath, L., Balakrishnan, H., Madden, S., & Girod, L. (2011). Accurate, low-energy trajectory mapping for mobile devices. In Proceedings of USENIX NSDI (pp. 267–280).Google Scholar
  27. 27.
    ThingMagic. Why use rfid. In
  28. 28.
    Wang, J., Gao, Q., Cheng, P., Yu, Y., Xin, K., & Wang, H. (2014). Lightweight robust device-free localization in wireless networks. IEEE Transactions on Industrial Electronics, 61(10), 5681–5689.CrossRefGoogle Scholar
  29. 29.
    Wang, J., Gao, Q., Wang, H., Cheng, P., & Xin, K. (2015). Device-free localization with multi-dimensional wireless link information. IEEE Transactions on Vehicular Technology, 64(1), 356–366.CrossRefGoogle Scholar
  30. 30.
    Wang, J., & Katabi, D. (2013). Dude, wheres my card? RFID positioning that works with multipath and non-line of sight. In Proceedings of ACM SigComm (pp. 51–62).Google Scholar
  31. 31.
    Wang, J., Xie, B., Fang, D., Chen, L.C., Xiaojiang, Xing, T., & Nie, W. (2015). Accurate device-free localization with little human cost. In Proceedings of ACM MobiCom Workshop .Google Scholar
  32. 32.
    Wang, Z., Liao, Z., Cao, Q., Qi, H., Wang, Z. (2014). Achieving k-barrier coverage in hybrid directional sensor networks. IEEE Transactions on Mobile Computing, 13(7), 1443–1455.CrossRefGoogle Scholar
  33. 33.
    Wen, Y., Tian, X., Wang, X., & Lu, S. (2015). Fundamental limits of RSS fingerprinting based indoor localization. In Proceedings of IEEE INFOCOM.Google Scholar
  34. 34.
    Xu, C., Firner, B., Moore, R.S., Zhang, Y., Trappe, W., Howard, R., Zhang, F., & An, N. (2013). Scpl: Indoor device-free multi-subject counting and localization using radio signal strength. In Proceedings of ACM IPSN (pp. 79–90).Google Scholar
  35. 35.
    Xu, C., Firner, B., Zhang, Y., Howard, R., Li, J., & Lin, X. (2012). Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of ACM IPSN (pp. 209–220).Google Scholar
  36. 36.
    Yang, L., Chen, Y., Li, X., Xiao, C., Li, M., & Liu, Y. (2014). Tagoram: Real-time tracking of mobile rfid tags to high precision using cots devices. In Proceedings of ACM MobiCom (pp. 237–248).Google Scholar
  37. 37.
    Yang, L., Han, J., Qiy, Y., Wang, C., Gux, T., & Liu, Y. (2011). Season: Shelving interference and joint identification in large-scale rfid systems. In Proceedings of IEEE InfoCom (pp. 3092–3100).Google Scholar
  38. 38.
    Yang, Z., Wang, Z., Zhang, J., Huang, C., & Zhang, Q. (2015). Wearables can afford: Light-weight indoor positioning with visible light. In Proceedings of ACM MobiSys (pp. 317–330).Google Scholar
  39. 39.
    Zhang, D., Liu, Y., Guo, X., & Ni, L. M. (2013). RASS: A real-time, accurate, and scalable system for tracking transceiver-free objects. IEEE Transactions on Parallel and Distributed Systems, 24(5), 996–1008.CrossRefGoogle Scholar
  40. 40.
    Zhang, D., Zhou, J., Guo, M., Cao, J., & Li, T. (2011). TASA: Tag-free activity sensing using rfid tag arrays. IEEE Transactions on Parallel and Distributed Systems (TPDS), 22(4), 558–570.CrossRefGoogle Scholar
  41. 41.
    Zhao, D., Ma, H., & Tang, S. (2014). Coupon: A cooperative framework for building sensing maps in mobile opportunistic networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 392–402.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Binbin Xie
    • 1
  • Dingyi Fang
    • 1
  • Tianzhang Xing
    • 1
  • Lichao Zhang
    • 1
  • Xiaojiang Chen
    • 1
    Email author
  • Zhanyong Tang
    • 1
  • Anwen Wang
    • 1
  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina

Personalised recommendations