A maximum likelihood estimator (MLE) is developed for determining the trajectory of a constant velocity target from data of incertain origin. The result is a nonlinear iterative algorithm that adaptively weights the data to make a probabilistic assignment of measurement to source. The Cramer-Rao bound (CRB) is derived and reveals the degradation expected in system performances due to the nonassociated nature of the data. The assumption of accurate knowledge of the noise variance is relaxed and an estimator of this parameter is developed. The resulting algorithm initially utilizes a broadly distributed weighting function which, as the estimate of the variance improves, 'tightens up' around the collection of measurements associated with the target. This adaptive windowing is experimentally shown to be robust in the sense that the state estimate used at each stage of the iteration process need not be refined to a high degree of accuracy to provide acceptable estimator performance.


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    Title :

    Trajectory and parameter estimation with measurements of uncertain origin


    Additional title:

    Trajektorien- und Parameterschaetzung unter Verwendung von Messungen mit unbekanntem Nullpunkt


    Contributors:
    Irza, J.W. (author) / Lindren, A.G. (author)

    Published in:

    Eighteenth ; 170-178


    Publication date :

    1984


    Size :

    9 Seiten, 9 Bilder, 12 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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