This chapter extends the estimation concepts presented previously to the case of dynamic (time‐varying) quantities. The estimation of the state vector of a stochastic linear dynamic system is considered. The state estimator for discrete‐time linear dynamic systems driven by white noise—the (discrete‐time) Kalman filter—is introduced and its properties are discussed. The continuous‐time case is considered, and an example that illustrates the discrete time Kalman filter is given. The issue of consistency of a dynamic estimator, which is crucial for evaluation of estimator optimality in every implementation, is discussed. The initialization of estimators and practical ways to make it consistent are presented. A problem solving section appears at the end of the chapter.
State Estimation in Discrete‐Time Linear Dynamic Systems
2002-01-04
68 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Extensions of Discrete‐Time Linear Estimation
Wiley | 2002
|Continuous‐Time Linear State Estimation
Wiley | 2002
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