From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective

  • Jihong Yu
  • Lin Chen


Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio frequency identification (RFID) applications. However, the existing estimation mechanisms are proposed for the static case where tag population remains constant, thus leaving the more challenging dynamic case unaddressed. This chapter introduces a generic framework of stable and accurate estimation schemes based on Kalman filter for both static and dynamic RFID systems. We first model the system dynamics as discrete stochastic processes and leverage the techniques in extended Kalman filter (EKF) and cumulative sum control chart (CUSUM) to estimate tag population for static/dynamic systems. By employing Lyapunov drift analysis, we characterise the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters. The relative estimation error is bounded and converged to zero at exponential rate.


  1. 1.
    RFID Journal, DoD releases final RFID policy. [Online]Google Scholar
  2. 2.
    RFID Journal, DoD reaffirms its RFID goals. [Online]Google Scholar
  3. 3.
    C.-H. Lee, C.-W. Chung, Efficient storage scheme and query processing for supply chain management using RFID, in ACM SIGMOD (ACM, New York, 2008), pp. 291–302Google Scholar
  4. 4.
    L.M. Ni, D. Zhang, M.R. Souryal, RFID-based localization and tracking technologies. IEEE Wirel. Commun. 18(2), 45–51 (2011)Google Scholar
  5. 5.
    P. Yang, W. Wu, M. Moniri, C.C. Chibelushi, Efficient object localization using sparsely distributed passive RFID tags. IEEE Trans. Ind. Electron. 60(12), 5914–5924 (2013)Google Scholar
  6. 6.
    RFID Journal, Wal-Mart begins RFID process changes. [Online]Google Scholar
  7. 7.
    M. Kodialam, T. Nandagopal, W.C. Lau, Anonymous tracking using RFID tags, in IEEE INFOCOM (IEEE, Piscataway, 2007), pp. 1217–1225Google Scholar
  8. 8.
    T. Li, S. Wu, S. Chen, M. Yang, Energy efficient algorithms for the RFID estimation problem, in IEEE INFOCOM (IEEE, Piscataway, 2010), pp. 1–9Google Scholar
  9. 9.
    C. Qian, H. Ngan, Y. Liu, L. M. Ni, Cardinality estimation for large-scale RFID systems. IEEE Trans. Parallel Distrib. Syst. 22(9), 1441–1454 (2011)Google Scholar
  10. 10.
    M. Shahzad, A.X. Liu, Every bit counts: fast and scalable RFID estimation, in ACM Mobicom (2012), pp. 365–376Google Scholar
  11. 11.
    Y. Zheng, M. Li, Zoe: fast cardinality estimation for large-scale RFID systems, in IEEE INFOCOM (IEEE, Piscataway, 2013), pp. 908–916Google Scholar
  12. 12.
    EPCglobal Inc., Radio-frequency identity protocols class-1 generation-2 UHF RFID protocol for communications at 860 mhz - 960 mhz version 1.0.9 [Online]Google Scholar
  13. 13.
    Y. Song, J.W. Grizzle, The extended Kalman filter as a local asymptotic observer for nonlinear discrete-time systems, in American Control Conference (IEEE, Piscataway, 1992), pp. 3365–3369Google Scholar
  14. 14.
    M. Kodialam, T. Nandagopal, Fast and reliable estimation schemes in RFID systems, in ACM Mobicom (ACM, New York, 2006), pp. 322–333Google Scholar
  15. 15.
    H. Han, B. Sheng, C.C. Tan, Q. Li, W. Mao, S. Lu, Counting RFID tags efficiently and anonymously, in IEEE INFOCOM (IEEE, Piscataway, 2010), pp. 1–9Google Scholar
  16. 16.
    V. Sarangan, M. Devarapalli, S. Radhakrishnan, A framework for fast RFID tag reading in static and mobile environments. Comput. Netw. 52(5), 1058–1073 (2008)Google Scholar
  17. 17.
    L. Xie, B. Sheng, C.C. Tan, H. Han, Q. Li, D. Chen, Efficient tag identification in mobile RFID systems, in IEEE INFOCOM (IEEE, Piscataway, 2010), pp. 1–9Google Scholar
  18. 18.
    Q. Xiao, B. Xiao, S. Chen, Differential estimation in dynamic RFID systems, in IEEE INFOCOM (IEEE, Piscataway, 2013), pp. 295–299Google Scholar
  19. 19.
    Q. Xiao, M. Chen, S. Chen, Y. Zhou, Temporally or spatially dispersed joint RFID estimation using snapshots of variable lengths, in ACM MobiHoc (ACM, New York, 2015), pp. 247–256Google Scholar
  20. 20.
    T. Morozan, Boundedness properties for stochastic systems, in Stability of Stochastic Dynamical Systems (Springer, Berlin, 1972), pp. 21–34Google Scholar
  21. 21.
    T.-J. Tarn, Y. Rasis, Observers for nonlinear stochastic systems. IEEE Trans. Autom. Control 21(4), 441–448 (1976)Google Scholar
  22. 22.
    K. Reif, S. Günther, E. Yaz Sr., R. Unbehauen, Stochastic stability of the discrete-time extended Kalman filter. IEEE Trans. Autom. Control 44(4), 714–728 (1999)Google Scholar
  23. 23.
    M.B. Rhudy, Y. Gu, Online stochastic convergence analysis of the Kalman filter. Int. J. Stoch. Anal. 2013, 240295 (2013)Google Scholar
  24. 24.
    K. Finkenzelle, RFID Handbook: Radio Frequency Identification Fundamentals and Applications (Wiley, Chichester, 2000)Google Scholar
  25. 25.
    V.F. Kolchin, B.A. Sevastyanov, V.P. Chistyakov, Random Allocation (Wiley, New York, 1978)Google Scholar
  26. 26.
    F. Gustafsson, F. Gustafsson, Adaptive Filtering and Change Detection (Wiley, New York, 2000)Google Scholar
  27. 27.
    E. Brodsky, B.S. Darkhovsky, Nonparametric Methods in Change Point Problems (Springer Science & Business Media, New York, 1993)Google Scholar
  28. 28.
    M. Basseville, I.V. Nikiforov, et al., Detection of Abrupt Changes: Theory and Application (Prentice Hall, Englewood Cliffs, 1993)Google Scholar
  29. 29.
    F. Spiring, Introduction to statistical quality control. Technometrics 49(1), 108–109 (2007)Google Scholar
  30. 30.
    M. Chen, W. Luo, Z. Mo, S. Chen, Y. Fang, An efficient tag search protocol in large-scale RFID systems with noisy channel, in IEEE/ACM TON (2015)Google Scholar
  31. 31.
    M. Shahzad, A.X. Liu, Expecting the unexpected: fast and reliable detection of missing RFID tags in the wild, in IEEE INFOCOM (2015), pp. 1939–1947Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Jihong Yu
    • 1
  • Lin Chen
    • 2
  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.Laboratoire de Recherche en InformatiqueUniversity of Paris-SudOrsayFrance

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