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Fusion Estimation for WSNs with Delayed Measurements

  • Wen-An Zhang
  • Bo Chen
  • Haiyu Song
  • Li Yu
Chapter
  • 557 Downloads

Abstract

In this chapter, a state fusion estimator design method will be introduced for multisensor systems with measurement delays, which is usually inevitable in sensor networks. Due to delays in the measurements, it is difficult to construct an innovation sequence that is still white Gaussian as usually does in the standard Kalman filter. Therefore, many research works have been devoted to the design of optimal linear estimators for time-delay systems by using the innovation analysis approach and linear matrix inequality approach [1–7]. For the multisensor case, the information fusion problem has been investigated in [8, 9] for linear stochastic systems with time-delayed measurements, where the observation delays were assumed to be constant.

Keywords

Information Fusion Linear Matrix Inequality Approach Innovation Sequence Multisensor System Linear Stochastic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zhang H, Xie L, Zhang D, Soh YC (2004) A reorganized innovation approach to linear estimation. IEEE Trans Autom Control 49(10):1810–1814MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen B, Yu L, Zhang WA (2011) Robust Kalman filtering for uncertain state delay systems with random observation delays and missing measurements. IET Control Theory Appl 5(17):1945–1954MathSciNetCrossRefGoogle Scholar
  3. 3.
    Dong H, Wang Z, Gao H (2010) Robust \(H_{\infty }\) filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts. IEEE Trans Signal Process 58(4):1957–1966MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang H, Feng G, Han C (2011) Linear estimation for random delay systems. Syst Control Lett 60(7):450–459MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Zhang H, Feng G, Duan G, Lu X (2006) \(H_{\infty }\) filtering for multiple-time-delay measurements. IEEE Trans Signal Process 54(5):1681–1688CrossRefGoogle Scholar
  6. 6.
    Ma L, Da F, Zhang KJ (2011) Exponential \(H_{\infty }\) filter design for discrete time-delay stochastic systems with markovian jump parameters and missing measurements. IEEE Trans Circuits Syst-I Regul Pap 58(5):994–1007MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yang F, Wang Z, Feng G, Liu X (2009) Robust filtering with randomly varying sensor delay: the finite-horizon case. IEEE Trans Circuits Syst-I Regul Pap 56(3):664–672MathSciNetCrossRefGoogle Scholar
  8. 8.
    Sun XJ, Deng ZL (2009) Information fusion wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements. IET Signal Process 3(5):403–415MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lv N, Sun SL (2009) Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements. In: Proceedings of IEEE conference on decision and control, Shanghai, Dec 2009, pp 7599–7602Google Scholar
  10. 10.
    Xia Y, Shang J, Chen J, Liu GP (2009) Networked data fusion with packet losses and variable delays. IEEE Trans Syst Man Cybern B Cybern 39(5):1107–1120CrossRefGoogle Scholar
  11. 11.
    Hounkpevi FO, Yaz EE (2007) Robust minimum variance linear state estimators for multiple sensors with different failure rates. Automatica 43(7):1274–1280MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Ahmad A, Gani M, Yang F (2008) Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks. Signal Process 88(8):1919–1928CrossRefzbMATHGoogle Scholar
  13. 13.
    Wang Z, Zhen Z, Zhang H, Chen Z (2009) Robust information fusion filtering method for discrete-time linear uncertain system. In: IEEE international conference on control and automation, Christchurch, Dec 2009, pp 1734–1738Google Scholar
  14. 14.
    Feng J, Wang Z, Zeng M (2013) Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises. Inf Fusion 14(1):78–86CrossRefGoogle Scholar
  15. 15.
    Gao H, Meng X, Chen T (2008) Stabilization of networked control systems with a new delay characterization. IEEE Trans Autom Control 53(9):2142–2148MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sun SL, Deng ZL (2004) Multi-sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1023MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kailath T, Syayed AH, Hassibi B (2000) Linear estimation. Prentice Hall, Upper Saddle RiverGoogle Scholar

Copyright information

© Science Press, Beijing and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Wen-An Zhang
    • 1
  • Bo Chen
    • 1
  • Haiyu Song
    • 2
  • Li Yu
    • 3
  1. 1.Department of AutomationZhejiang University of TechnologyHangzhouChina
  2. 2.Zhejiang Uni. of Finance & EconomicsHangzhouChina
  3. 3.Zhejiang University of TechnologyHangzhouChina

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