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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    Title :

    Information fusion based on fast covariance intersection filtering


    Contributors:
    Niehsen, W. (author)


    Publication date :

    2002-01-01


    Size :

    213215 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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