Radar constant false alarm rate (CFAR) detection is addressed in this correspondence. Motivated by the frequently encountered problem of clutter-edge heterogeneity, we model the secondary data as a probability mixture and impose a hierarchical model for the inference problem. A two-stage CFAR detector structure is proposed. Empirical Bayesian inference is adopted in the first stage for training data selection followed by a CFAR processor using the identified homogeneous training set for target detection. One of the advantages of the proposed algorithm is its inherent adaptivity; i.e., the threshold setting is much less sensitive to the nonstationary environment compared with other standard CFAR procedures.


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    Titel :

    Adaptive CFAR detection for clutter-edge heterogeneity using Bayesian inference


    Beteiligte:
    Biao Chen (Autor:in) / Varshney, P.K. (Autor:in) / Michels, J.H. (Autor:in)


    Erscheinungsdatum :

    01.10.2003


    Format / Umfang :

    381391 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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