The Dempster-Shafer method, fuzzy reasoning and backpropagation neural networks were applied to data fusion for automatic target recognition of airborne targets using three knowledge sources: a continuous wave (CW) coherent (X band) radar, which provides high resolution Doppler signature measurements, together with a surveillance radar, which provides positional information of airborne targets, and a priori information of flight times of targets flying regular flight paths, obtained from Adelaide Airport (Australia) flight time tables. The target classification was restricted to jet or propeller-driven aircrafts. A comparison is made between the three data-fusion techniques, on the trial data obtained. The method using neural networks were better then the other two methods. The neural networks need less computation time because of parallel processing and they obtain the uncertainty of the knowledge source indirectly without a priori knowledge or calculation.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data fusion using sensor data and a priori information


    Additional title:

    Flugzeugerkennung und Flugzielerkennung aus zwei unterschiedlichen Radarmessungen


    Contributors:

    Published in:

    Publication date :

    1996


    Size :

    11 Seiten, 7 Bilder, 9 Tabellen, 11 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Perception and autonomous navigation using a priori data

    Conrad,K.L. / Galloway,J.R. / Irwin,W.P. et al. | Automotive engineering | 2006


    Perception and Autonomous Navigation Using a Priori Data

    Little, Charles Q. / Kuntimad, Govindaraj / Jack, James T. et al. | SAE Technical Papers | 2006



    Factoring a priori classifier performance into decision fusion

    Goebel, K. / Mysore, S.P. | Tema Archive | 2002