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- W186768504 abstract "This chapter reviews the status of observer theory and presents the results obtained in stochastic observer theory as applied to discrete-time linear systems. The Kalman filter solves the problem of state estimation in the mean-square sense for linear discrete-time stochastic systems, numerical and computational problems associated with the real-time implementation of Kalman filters have led researchers to seek out computationally simpler solutions to the minimum mean-square state-estimation problem. The chapter reviews some of the alternate approaches to the discrete-time state-estimation problem based on the extension of Luenberger's observer theory to stochastic systems. The chapter discusses the notion of an observer for discrete stochastic systems. It presents a method of constructing a reduced-order observer estimator. The case of some perfect measurements is considered. The interpretations of Brammer's optimal observer, namely, a Kalman-type algorithm and a Luenberger-type algorithm have been presented. Computational advantages of the reduced-order observer algorithm have been presented." @default.
- W186768504 created "2016-06-24" @default.
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- W186768504 date "1976-01-01" @default.
- W186768504 modified "2023-09-23" @default.
- W186768504 title "Discrete-Time Optical Stochastic Observers" @default.
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- W186768504 doi "https://doi.org/10.1016/b978-0-12-012712-2.50011-1" @default.
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