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Further Results on Robust Variance-Constrained Filtering for Uncertain Stochastic Systems with Missing Measurements

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Abstract

This paper revisits the problem of robust filtering for uncertain discrete-time stochastic systems with missing measurements. The measurements of the system may be unavailable at any sample time. Our aim is to design a new filter such that the error state of the filtering process is mean-square bounded. Furthermore, the steady-state variance of the estimation error of each state does not exceed the individual prescribed upper bound subject to all admissible uncertainties and all possible incomplete observations. It is shown that the design of a robust filter can be carried out by directly solving a set of linear matrix inequalities. The nonsingular assumption on the system matrix A and the inequality which is used to handle the uncertainties are not necessary in the derivation process of our results. Thus, it is expected that a less conservative condition can be obtained. The advantage of the new method is demonstrated via an illustrative example.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, P.R. China

    Yijing Wang & Zhiqiang Zuo

  2. Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong

    Zhiqiang Zuo

Authors
  1. Yijing Wang
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  2. Zhiqiang Zuo
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Correspondence to Zhiqiang Zuo.

Additional information

This work is supported by the National Natural Science Foundation of China No. 60774039 and No. 60974024, CityU Research Enhancement fund 9360127, and CityU SRG 7002355.

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Wang, Y., Zuo, Z. Further Results on Robust Variance-Constrained Filtering for Uncertain Stochastic Systems with Missing Measurements. Circuits Syst Signal Process 29, 901–912 (2010). https://doi.org/10.1007/s00034-010-9178-4

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  • DOI: https://doi.org/10.1007/s00034-010-9178-4

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