Robust finite horizon minimax filtering for discrete-time stochastic uncertain systems

Myung Gon Yoon; Valery A. Ugrinovskii; Ian R. Petersen

doi:10.1016/j.sysconle.2003.11.004

Robust finite horizon minimax filtering for discrete-time stochastic uncertain systems

Myung Gon Yoon^*
, Valery A. Ugrinovskii
, Ian R. Petersen

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

Abstract

We study a finite-horizon robust minimax filtering problem for time-varying discrete-time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which the stochastic noises, driving the system, are defined. The optimal minimax filter has been found by applying techniques of risk-sensitive LQG control. The structure and properties of resulting filter are analyzed and compared to H_∞ and Kalman filters.

Original language	English
Pages (from-to)	99-112
Number of pages	14
Journal	Systems and Control Letters
Volume	52
Issue number	2
DOIs	https://doi.org/10.1016/j.sysconle.2003.11.004
Publication status	Published - Jun 2004
Externally published	Yes

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10.1016/j.sysconle.2003.11.004

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title = "Robust finite horizon minimax filtering for discrete-time stochastic uncertain systems",

abstract = "We study a finite-horizon robust minimax filtering problem for time-varying discrete-time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which the stochastic noises, driving the system, are defined. The optimal minimax filter has been found by applying techniques of risk-sensitive LQG control. The structure and properties of resulting filter are analyzed and compared to H∞ and Kalman filters.",

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author = "Yoon, \{Myung Gon\} and Ugrinovskii, \{Valery A.\} and Petersen, \{Ian R.\}",

year = "2004",

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AU - Ugrinovskii, Valery A.

AU - Petersen, Ian R.

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N2 - We study a finite-horizon robust minimax filtering problem for time-varying discrete-time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which the stochastic noises, driving the system, are defined. The optimal minimax filter has been found by applying techniques of risk-sensitive LQG control. The structure and properties of resulting filter are analyzed and compared to H∞ and Kalman filters.

AB - We study a finite-horizon robust minimax filtering problem for time-varying discrete-time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which the stochastic noises, driving the system, are defined. The optimal minimax filter has been found by applying techniques of risk-sensitive LQG control. The structure and properties of resulting filter are analyzed and compared to H∞ and Kalman filters.

KW - H filter

KW - Kalman filter

KW - Risk-sensitive control

KW - Robust filtering

UR - https://www.scopus.com/pages/publications/2342636410

U2 - 10.1016/j.sysconle.2003.11.004

DO - 10.1016/j.sysconle.2003.11.004

M3 - Article

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VL - 52

SP - 99

EP - 112

JO - Systems and Control Letters

JF - Systems and Control Letters

IS - 2

ER -

Robust finite horizon minimax filtering for discrete-time stochastic uncertain systems

Abstract

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