A fundamental issue in knowledge-aided space-time adaptive processing (KA-STAP) is to determine the degree of accuracy of the a~priori knowledge and the optimal emphasis that should be placed on it. In KA-STAP, the a priori knowledge consists usually of an initial guess of the clutter covariance matrix. This can be obtained either by previous radar probings or by a map-based study. We consider a linear combination of the a~priori clutter covariance matrix with the sample covariance matrix obtained from secondary data, and derive an optimal weighting factor on the a priori knowledge by a maximum likelihood (ML) approach. The performance of the ML approach for KA-STAP is evaluated based on numerically simulated data.


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    Titel :

    Knowledge-Aided Space-Time Adaptive Processing


    Beteiligte:
    Xumin Zhu, (Autor:in) / Jian Li, (Autor:in) / Stoica, Petre (Autor:in)


    Erscheinungsdatum :

    2011-04-01


    Format / Umfang :

    1861129 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch