Seven filter algorithms were presented in a recent survey paper [2], and were compared computationally (operations count) when relatively few observations were to be processed. These algorithms are elaborated further in this paper. Details of the computations are presented, and it is shown that for problems with even moderately large amounts of data, the information matrix and square-root information matrix formulations are computationally more efficient than the other methods considered (conventional Kalman, stabilized Kalman, and square-root covariance mechanizations). It is pointed out that Schmidt's matrix factorization-Householder transformation technique leads to the same equations as those obtained via Potter's method. Several improvements in the equation mechanization are given.


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    Title :

    A Comparison of Discrete Linear Filtering Algorithms


    Contributors:


    Publication date :

    1973-01-01


    Size :

    2229077 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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