Presented here is the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature is that we allow the knowledge sources to supply their information in the form of uncertain implication rules. How these rules can be elegantly handled within the framework of the belief function theory is described. A small scale, practical example for target identification is worked through in detail to clarify the theory for future users.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Target identification using belief functions and implication rules


    Contributors:
    Ristic, B. (author) / Smets, P. (author)


    Publication date :

    2005-07-01


    Size :

    426745 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Using Belief Functions to Represent Degrees of Belief

    Curley, Shawn P. | Online Contents | 1994


    DRIVING RISK ASSESSMENT WITH BELIEF FUNCTIONS

    Daniel, J. / Lauffenburger, J. / Bernet, S. et al. | British Library Conference Proceedings | 2013


    Driving risk assessment with belief functions

    Daniel, Jeremie / Lauffenburger, Jean-Philippe / Bernet, Sacha et al. | IEEE | 2013


    Shape from Shading Using Probability Functions and Belief Propagation

    Wilhelmy, J. / Krüger, J. r. | British Library Online Contents | 2009