Data association is the process of relating sensor measurements in a data fusion system. It can be structured in a basic framework very similar to that of the classic traveling salesman problem. The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge-Kutta method and Aiyer's network structure. The neural data association is then applied to the problem of multiple-target tracking (MTT). The proposed neural MTT system consists of a modified Hough transform track initiator, a Kalman filter state estimator and the Hopfield probabilistic data association. Real-life air surveillance data are used to evaluate the practicality of the neural MTT system, and the results show that the neural system works efficiently in real-life tracking environments.


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    Title :

    Neural network data association with application to multiple-target tracking


    Additional title:

    Datenverknüpfung in einem neuronalen Netz und ihre Anwendung zur Verfolgung der Bahnen mehrerer Ziele


    Contributors:
    Leung, H. (author)

    Published in:

    Optical Engineering ; 35 , 3 ; 693-700


    Publication date :

    1996


    Size :

    8 Seiten, 6 Bilder, 3 Tabellen, 22 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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