Because of the statistical nature nature of many types of clutter, a radar target detector must set a fairly high threshold in order to order to maintain a reasonable false-alarm rate. However, weak targets are usually missed for the above threshold detector. This paper presents an effective detector, which can be considered as a two-dimensional feature matching filter for radar signals. The feature extraction is performed by Hopfield neural networks and the feature integration is finished by a multilayer perceptron. In order to overcome the local optimum problem, a novel modification which is called energy comparing method is introduced into the Hopfield model dynamic equation to find the global optimum. By testing with the real radar return data in a low signal-to-clutter ratio, the detector presented in this paper has more advantages than the conventional threshold detector.<>


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    Title :

    The neural network method for radar weak target detection


    Contributors:
    Hu Wei-Dong (author) / Yu Wen-Xian (author) / Guo Gui-Rong (author)


    Publication date :

    1994-01-01


    Size :

    501699 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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