Information fusion based on Kalman filtering often suffers from the lack of knowledge about cross correlations between the noise-corrupted signal sources. Covariance intersection filtering provides a general framework for information fusion with incomplete knowledge about the signal sources since it yields consistent estimates for any degree of cross correlation. However, covariance intersection filtering requires optimization of a nonlinear cost function which is a significant drawback with respect to computational complexity. Therefore, a fast covariance intersection algorithm is developed and investigated based on simulation results.


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    Titel :

    Information fusion based on fast covariance intersection filtering


    Beteiligte:
    Niehsen, W. (Autor:in)


    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    213215 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Information Fusion based on Fast Covariance Intersection Filtering

    Niehsen, W. / International Society of Information Fusion / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2002



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