The local behavior of a maximum likelihood estimator that adaptively weights data of uncertain origin to make a probabilistic measurement-to-track assignment is examined. The results are placed in the framework of the classic estimation theory of Fisher and Cramer. The Cramer-Rao bound is derived and the MLS error level is compared with this lower bound and the level achieved with known data association. Deterioration in performance was found to depend solely on the false-detection to valid-contact ratio, and the actual (measured) covariance matrix is a scalar multiple of the covariance matrix computed for associated data.


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    Title :

    Trajectory Estimation with Uncertain and Nonassociated Data


    Contributors:


    Publication date :

    1986-01-01


    Size :

    1842065 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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