Robust filtering for bilinear uncertain stochastic discrete-time systems

Zidong Wang; Hong Qiao

doi:10.1109/78.984737

Robust filtering for bilinear uncertain stochastic discrete-time systems

Zidong Wang
, Hong Qiao

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

88 Citations (Scopus)

Abstract

This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method.
© 2002 IEEE

Original language	English
Pages (from-to)	560-567
Journal	IEEE Transactions on Signal Processing
Volume	50
Issue number	3
DOIs	https://doi.org/10.1109/78.984737
Publication status	Published - Mar 2002
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the City University of Hong Kong, the University of Kaiserslautern, Germany, and the Alexander von Humboldt Foundation, Germany. The associate editor coordinating the review of this paper and approving it for publication was Dr. Gonzalo R. Arce.

Research Keywords

Bilinear stochastic systems
Discrete-time systems
Quadratic matrix inequalities
Robust filtering
Uncertain systems

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abstract = "This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method. {\textcopyright} 2002 IEEE",

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N2 - This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method. © 2002 IEEE

AB - This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method. © 2002 IEEE

KW - Bilinear stochastic systems

KW - Discrete-time systems

KW - Quadratic matrix inequalities

KW - Robust filtering

KW - Uncertain systems

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DO - 10.1109/78.984737

M3 - RGC 21 - Publication in refereed journal

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SP - 560

EP - 567

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 3

ER -

Robust filtering for bilinear uncertain stochastic discrete-time systems

Abstract

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