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H Fusion Estimation for WSNs with Nonuniform Sampling Rates

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

Abstract

In Chap.  3, Kalman fusion filters are designed for sensor networks with nonuniform estimation rates. Though the celebrated Kalman filtering is commonly regarded as one of the most popular and useful approaches to filtering problem, it usually assumes that the system model is precise and that the external noises are white Gaussian. Such assumptions may not hold in many practical applications. In this case, one may resort to other useful filtering algorithms. The \(H_{\infty }\) filtering is among these useful algorithms, it provides a guaranteed noise attenuation level and does not have to know exact statistical information of external noises.

Keywords

Sensor Network Continuous Stir Tank Reactor Packet Loss Probability Average Dwell Time Random Packet Loss 
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.
    Shi P, de Souza CE, Xie LH (1993) Robust \(H_{\infty }\) filtering for uncertain systems with sampled-data measurements. In: Proceedings of the 32nd IEEE conferences on decision and control, San Antonlo, pp 793–798Google Scholar
  2. 2.
    Shi P (1998) Filtering on sampled-data systems with parametric uncertainty. IEEE Trans Autom Control 43(7):1022–1027MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Mirkin L, Palmor ZJ (1999) On the sampled-data \(H_{\infty }\) filtering problem. Automatica 35(5):895–905MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Nguang SK, Shi P (2000) Nonlinear \(H_{\infty }\) filtering of sampled-data systems. Automatica 36(2):303–310MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Wang ZD, Huang B, Huo PJ (2001) Sampled-data filtering with error ovariance assignment. IEEE Trans Signal Process 49(3):666–670CrossRefGoogle Scholar
  6. 6.
    Xu SY, Chen TW (2003) Robust \(H_{\infty }\) filtering for uncertain impulsive stochastic systems under sampled measurements. Automatica 39(3):509–516MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Teixeira BOS, Santillo MA, Erwin RS, Bernstein DS (2008) Sapcecraft tracking using sampled-data Kalman filters. IEEE Control Syst Mag 28(4):78–94MathSciNetCrossRefGoogle Scholar
  8. 8.
    Sun WQ, Nagpal KM, Khargonekar PP (1993) \(H_{\infty }\) control and filtering for sampled-data systems. IEEE Trans Autom Control 38(8):1162–1175MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Li WH, Shah SL, Xiao DY (2008) Kalman filters in non-uniformly sampled multirate systems: for FDI and beyond. Automatica 44(1):199–208MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Suplin V, Fridman E, Shaked U (2007) Sampled-data \(H_{\infty }\) control and filtering: nonuniform uncertain sampling. Automatica 43(6):1072–1083MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Wu JL, Chen XM, Gao HJ (2010) \(H_{\infty }\) filtering with stochastic sampling. Signal Process 90(4):1131–1145CrossRefzbMATHGoogle Scholar
  12. 12.
    Shen B, Wang ZD, Liu XH (2011) A stochastic sampled-data approach to distributed \(H_{\infty }\) filtering in sensor networks. IEEE Trans Circuits Syst-I Regul Pap 58(9):2237–2246MathSciNetCrossRefGoogle Scholar
  13. 13.
    Shi P, Luan XL, Liu F (2012) \(H_{\infty }\) filtering for discrete-time systems with stochastic incomplete measurement and mixed delays. IEEE Trans Ind Electron 59(6):2732–2739CrossRefGoogle Scholar
  14. 14.
    Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan MI, Sastry SS (2004) Kalman filtering with intermittent observations. IEEE Trans Autom Control 49(9):1453–1464MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang ZD, Ho DWC, Liu XH (2003) Variance-constrained filtering for uncertain stochastic systems with missing measurements. IEEE Trans Autom Control 48(7):1254–1258MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sun SL, Xie LH, Xiao WD, Soh YC (2008) Optimal linear estimation for systems with multiple packet dropouts. Automatica 44(7):1333–1342MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Fridman E, Seuret A, Richard JP (2004) Robust sampled-data stabilization of linear systems: an input delay approach. Automatica 40(8):1441–1446MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Fujioka H (2009) Stability analysis of systems with aperiodic sample-and-hold devices. Automatica 45(3):771–775MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Oishi H, Fujioka H (2010) Stability and stabilization of aperiodic sampled-data control systems using robust linear matrix inequalities. Automatica 46(8):1327–1333MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Shen B, Wang ZD, Hung YS (2010) Distributed \(H_{\infty }\)-consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case. Automatica 46(10):1682–1688MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Zhang WA, Yu L (2007) Output feedback stabilization of networked control systems with packet dropouts. IEEE Trans Autom Control 52(9):1705–1710MathSciNetCrossRefGoogle Scholar
  22. 22.
    de Oliveira, MC, Bernussou J, Geromel JC (1999) A new discrete-time robust stability condition. Syst Control Lett 37(4):261–265MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Klatt KU, Engell S (1998) Gain-scheduling trajectory control of a continuous stired tank reactor. Comput Chem Eng 22(4/5):491–502CrossRefGoogle 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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