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filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

Author

Listed:
  • Zhou Gu
  • Shumin Fei
  • Dong Yue
  • Engang Tian

Abstract

This paper deals with the problem of H∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

Suggested Citation

  • Zhou Gu & Shumin Fei & Dong Yue & Engang Tian, 2014. "filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1356-1363, July.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:7:p:1356-1363
    DOI: 10.1080/00207721.2013.871372
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