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State estimation with quantised sensor information in wireless sensor networks

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Abstract

The problem of state estimation with quantised measurements is considered for general vector state-vector observation model in wireless sensor networks (WSNs), which broadens the scope of sign of innovations Kalman filtering (SOI-KF) and multiple-level quantised innovations Kalman filter (MLQIKF). Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces a novel decentralised unscented Kalman filtering (UKF) estimators based on quantised measurement innovations. In the quantisation approach, the region of a measurement innovation is partitioned into L contiguous, non-overlapping intervals. After quantised, the measurement information is broadcasted by using a variable number of bytes coding method. A filtering algorithm for general vector state-vector observation case is developed based on the quantised measurement information. Performance analysis and Monte Carlo simulations reveal that under the same bandwidth constraint condition, the performance of novel quantised UKF tracker, indeed better than those of SOI-KF and MLQIKF in error covariance matrix (ECM) and root mean-square error (RMSE) and almost identical to these of an UKF based on analogue-amplitude observations.

References

    1. 1)
      • 1 onward link is available for this reference.
      • CrossRef
    2. 2)
      • 1 onward link is available for this reference.
      • CrossRef
    3. 3)
      • 1 onward link is available for this reference.
      • CrossRef
    4. 4)
      • 1 onward link is available for this reference.
      • CrossRef
    5. 5)
      • 1 onward link is available for this reference.
      • CrossRef
    6. 6)
      • 1 onward link is available for this reference.
      • CrossRef
    7. 7)
      • Sun, S., Lin, J., Xie, L., Xiao, W.: `Quantized Kalman filtering', Proc. 22nd IEEE Int. Symp. on Intelligent Control Part of IEEE Multi-Conf. on Systems and Control, October 2007, Singapore, p. 1–3.
    8. 8)
      • Karlsson, R., Gustafsson, F.: `Filtering and estimation for quantized sensor information', Technical report LiTH-ISY-R2674, January 2005.
    9. 9)
      • Duan, Z., Jilkov, V.P., Li, X.R.: `State estimation with quantized measurements: approximate MMSE approach', Proc. 11th Int. Conf. on Information Fusion, 2008.
    10. 10)
      • R.E. Curry . (1970) Estimation and control with quantized measurements.
    11. 11)
      • 1 onward link is available for this reference.
      • CrossRef
    12. 12)
      • Karlsson, R., Gustafsson, F.: `Particle filtering for quantized sensor information', Proc. 13th European Signal Processing Conf., EUSIPCO, September 2005, Antalya, Turkey.
    13. 13)
      • 1 onward link is available for this reference.
      • CrossRef
    14. 14)
      • Ribeiro, A.: `Distributed quantization–estimation for wireless sensor networks', 2005, Master, The University of Minnesota.
    15. 15)
      • 1 onward link is available for this reference.
      • CrossRef
    16. 16)
      • You, K., Xie, L., Sun, S., Xiao, W.: `Multiple-level quantized innovation kalman filter', Proc. 17th Int. Federation of Automatic Control, 2008, Seoul, Korea, p. 1420–1425.
    17. 17)
      • B. Ristic , S. Arulampalam , N. Gordon . (2004) Beyond the Kalman filter: particle filters for tracking applications.
    18. 18)
      • S. Kay . (1993) Fundamentals of statistical signal processing estimation theory.
    19. 19)
      • S. Ross . (1996) Stochastic processes.
    20. 20)
      • 1 onward link is available for this reference.
      • CrossRef
    21. 21)
      • Zoghi, M.R., Kahaei, M.H.: `Sensor selection for target tracking in WSN using modified INS Algorithm', Proc. Third Int. Conf. on Information and Communication Technologies: From Theory to Applications, 2008, p. 1–6.

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