In higher order Kalman filtering applications the analyst often has very little insight into the nature of the observability of the system. For example, there are situations where the filter may be estimating certain linear combinations of state variables quite well, but this is not apparent from a glance at the error covariance matrix. It is shown here that the eigenvalues and eigenvectors of the error covariance matrix, when properly normalized, can provide useful information about the observability of the system.


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    Title :

    Observability, Eigenvalues, and Kalman Filtering


    Contributors:

    Published in:

    Publication date :

    1983-03-01


    Size :

    1406907 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English