The use of a Kalman filter in an applications problem requires a detailed model of both the system dynamics and the measurement dynamics. The model for many problems may be extremely large in dimensionality. However, in many instances one has a limited computer capability and, thus, must purposely introduce modeling errors into the filter in order to gain a computational advantage. However, as is well known, this may lead to the phenomenon of filter divergence. This paper considers the development of equations which allow one to evaluate a filter of reduced state. The equations are based upon using covariance analysis techniques in order to determine the true root-mean-square estimation error. These equations are computationally more advantageous than others appearing in the literature.


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    Title :

    Performance Evaluation of Suboptimal Filters


    Contributors:

    Published in:

    Publication date :

    1975-05-01


    Size :

    1213307 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English