A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost, as compared with existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.


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    Titel :

    Low-Complexity Constrained Adaptive Reduced-Rank Beamforming Algorithms


    Beteiligte:
    Lei Wang (Autor:in) / DeLamare, Rodrigo C. (Autor:in)


    Erscheinungsdatum :

    01.10.2013


    Format / Umfang :

    1833354 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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