This paper deals with the estimation of the clutter covariance matrix in airborne radar space-time adaptive processing (STAP). Based on the persymmetry property, a novel STAP method, referred to as persymmetric extended factored processing (Per-EFA), is derived, which can make a more intensive use of the secondary data and improve the STAP performance in training-limited scenarios. Simulation results demonstrate the effectiveness of this method.


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    Titel :

    Improving EFA-STAP performance using persymmetric covariance matrix estimation


    Beteiligte:
    Yalong Tong, (Autor:in) / Tong Wang, (Autor:in) / Jianxin Wu, (Autor:in)


    Erscheinungsdatum :

    2015-04-01


    Format / Umfang :

    2434684 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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