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Filter designing with finite packet losses and its application for stochastic systems

Filter designing with finite packet losses and its application for stochastic systems

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Packet losses are a general problem in networked control system and wireless sensor networks (WSNs). In order to increase estimation accuracy and reliability, a filter with finite packet losses called minimum variance filter (MVF) is designed for stochastic systems. The authors develop a packet loss model, and design a novel MVF based on the orthogonal analysis approach. The proposed filters rely only on the packet arrival probability at each time instant and do not need to know whether the measurement is received at a particular time instant. However, the proposed MVF is not applicable to non-linear cases. So an extended MVF is derived for stochastic non-linear systems and applied to track a moving target in WSNs. Simulation results show that, compared to existing methods, the proposed MVFs have superior performance.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Ling, Q., Lemmon, M.: `Soft real-time scheduling of networked control systems with dropouts governed by a Markov chain', American Control Conf., 6 June 2003, Denver, p. 4845–4550.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • Liu, X.H., Goldsmith, A.: `Kalman filtering with partial observation losses', 43rdIEEE Conf. on Decision and Control, 14–17 December 2004, p. 4180–4186.
    13. 13)
    14. 14)
      • Moayedi, M., Soh, Y., Foo, Y.: `Optimal Kalman filtering with random sensor delays, packet dropouts and missing measurements', American Control Conf., 2009, ACC'09, p. 3405–3410.
    15. 15)
      • Schenato, L.: `Optimal sensor fusion for distributed sensors subject to random delay and packet loss', Proc. 46th IEEE Conf. on Decision and Control, 12–14 December 2007, New Orleans, LA, USA, p. 1547–1552.
    16. 16)
      • Liu, Y., Xu, B., Feng, L.: `Distributed IMM filter based dynamic-group scheduling scheme for maneuvering target tracking in wireless sensor network', Second Int. Cong. on Image and Signal Processing, 17–19 October 2009, Tianjin, Chian, p. 1–5.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • Y. Bar-Shalom , X. Li , T. Kirubarajan . (2001) Estimation with applications to tracking and navigation.
    21. 21)
    22. 22)
      • Bhardwaj, M.H.: `Bounding the lifetime of sensor networks via optimal role assignments', Ann. Joint Conf. on IEEE Computers and Communications Society, 23–27 June 2002, p. 1587–1596, vol. 3.
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