Titlebook: Robust Kalman Filtering for Signals and Systems with Large Uncertainties; Ian R. Petersen,Andrey V. Savkin Book 1999 Springer Science+Busi

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Introduction,gnal model is a linear but possibly time-varying linear system. Also, an approximate version of the Kalman Filter referred to as the “Extended Kalman Filter” can be applied in the case of a nonlinear signal model; e.g., see [2].

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Set-Valued State Estimation with Structured Uncertainty,ractable solution to be obtained for the set-valued state estimation problem in the case of structured uncertainty. Such problems have been found to be intractable using other representations of structured uncertainty.

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Book 1999 the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter ha

Magisterial

Introduction,near dynamical system driven by stochastic white noise processes, the Kalman Filter provides a method for constructing an optimal estimate of the system state. Thus, the Kalman Filter provides an optimal way of extracting a signal from noise by exploiting a state space signal model. A key feature of

雜役

apropos

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Discrete-Time Set-Valued State Estimation,ur approach is the deterministic interpretation of the discrete-time Kalman Filter given in [16]. In [16], the Kalman Filter is shown to give a state estimate in the form of an ellipsoidal set of all possible states consistent with the given process measurements and a deterministic description of th