A growing memory discrete dynamic model for performing temporal extrapolations along a predetermined path in a random field is presented. This dynamic model is used to drive a linear system that is itself driven by discrete white noise. The coupled system is used to derive a state estimation scheme that recursively processes noisy measurements of the system. Using the dynamic model as a reference (truth) model, the authors develop a covariance analysis to measure the estimation errors that occur when the dynamics along the path through the field are modeled as a Markov linear model and state estimation is performed using discrete Kalman filtering. The performance evaluation of an inertial navigation system influenced by the Earth's gravity field aboard a maneuvering ship is provided as a specific illustrative example.


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    Titel :

    State estimation and divergence analysis


    Weitere Titelangaben:

    Zustandsabschaetzung und Divergenz-Analyse


    Beteiligte:
    Catlin, D.E. (Autor:in) / Geddes, R.L. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    1984


    Format / Umfang :

    9 Seiten, 9 Quellen



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch