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.


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    Title :

    Adaptive Kalman Filter for Detectable Linear Time-Invariant Systems


    Contributors:

    Published in:

    Publication date :

    2019-10-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English