A new constant false-alarm rate (CFAR) detector for non-Rayleigh data, based on fuzzy statistical normalization, is proposed. The proposed detector carries out the detection with two stages. The first stage of the fuzzy statistical normalization CFAR processor is background level estimation, based on fuzzy statistical normalization. The second stage is signal detection, based on the original data and the defuzzification normalized data. Performance comparisons are carried out to validate the superiority of the proposed CFAR detector.


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    Title :

    Fuzzy statistical normalization CFAR detector for non-rayleigh data


    Contributors:
    Yanwei Xu (author) / Chaohuan Hou (author) / Shefeng Yan (author) / Jun Li (author) / Chengpeng Hao (author)


    Publication date :

    2015-01-01


    Size :

    3110216 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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