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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Knowledge-Aided Space-Time Adaptive Processing


    Contributors:
    Xumin Zhu, (author) / Jian Li, (author) / Stoica, Petre (author)


    Publication date :

    2011-04-01


    Size :

    1861129 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English