A stable, quadratically convergent numerical algorithm is presented for computing the steady-state covariance and gain matrices of the Kalman filter. The method is more rapidly convergent than standard Riccati integration techniques and is easier to implement than existing eigenvalue-eigenvector algorithms. The quadratic convergence is proved analytically and illustrated by a numerical example.<>


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    Title :

    Efficient numerical algorithm for steady-state Kalman covariance


    Contributors:


    Publication date :

    1988-11-01


    Size :

    239746 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English