A novel covariance matching technique is proposed for estimating the states and unknown entries of the process and measurement noise covariance matrices for additive white Gaussian noise elements in a linear Kalman filter. Under this assumption of detectability (that is, unobservable modes remain stable), the stability and convergence properties of the covariance matching Kalman filter are established. It is shown that the measurement covariance matrix cannot be unambiguously estimated if the measurement model contains linearly dependent measurements. Monte Carlo simulations evaluate the numerical properties of the proposed algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Kalman Filter for Detectable Linear Time-Invariant Systems


    Beteiligte:
    Moghe, Rahul (Autor:in) / Zanetti, Renato (Autor:in) / Akella, Maruthi R. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-05-24


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Kalman Filter for Linear Fractional Order Systems

    Koh, B S | Online Contents | 2012


    Adaptive Kalman Filter Based Freeway Travel Time Estimation

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    Vector Adaptive Approximated Kalman Filter

    Krasheninnikov, V. R. / Menzorov, A. V. | British Library Online Contents | 1996