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.


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    Titel :

    State Estimation in Discrete‐Time Linear Dynamic Systems


    Beteiligte:


    Erscheinungsdatum :

    2002-01-04


    Format / Umfang :

    68 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Extensions of Discrete‐Time Linear Estimation

    Bar‐Shalom, Yaakov / Li, X.‐Rong / Kirubarajan, Thiagalingam | Wiley | 2002



    Continuous‐Time Linear State Estimation

    Bar‐Shalom, Yaakov / Li, X.‐Rong / Kirubarajan, Thiagalingam | Wiley | 2002