The classification of ship targets using low resolution down-range radar profiles together with preprocessing and neural networks is investigated. An implementation of the Fourier-modified discrete Mellin transform is used as a means for extracting features which are insensitive to the aspect angle of the radar. Kohonen's self-organizing map with learning vector quantization (LVQ) is used for the classification of these feature vectors. The use of a feedforward network trained with the backpropagation algorithm is also investigated. The classification system is applied to both simulated and real data sets. Classification accuracies of up to 90% are reported for the real data, provided target aspect angle information is available to within an error not exceeding 30 deg.


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    Title :

    Ship target recognition using low resolution radar and neural networks


    Contributors:


    Publication date :

    1999-04-01


    Size :

    404086 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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