An implementation of the multistage Weiner filter (MWF) is developed for constrained filtering applications, such as radar surveillance, that require the formation of many filter vectors. The MWF is a "signal-dependent" reduced rank adaptive filter, which means that it uses the steering vector to form its basis for rank reduction. Signal-dependent processing provides a performance improvement over signal-independent methods, but typically incurs a computational burden that increases linearly with the number of filters. This paper describes a computationally efficient implementation of the MWF, based on the method of conjugate gradients (CG), and shows the relationship between MWF and CG. The CG-based technique uses a single SVD to impose a diagonal structure on the data matrix, and realizes an order-of-magnitude speed improvement over the conventional MWF.


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    Titel :

    Efficient adaptive reduced-rank multibeam processing


    Beteiligte:
    Weippert, M.E. (Autor:in) / Hiemstra, J.D. (Autor:in) / Goldstein, J.S. (Autor:in) / Sabio, V.J. (Autor:in) / Zoltowski, M.D. (Autor:in) / Reed, I.S. (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    440540 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Optimal and adaptive reduced-rank STAP

    Guerci, J.R. / Goldstein, J.S. / Reed, I.S. | IEEE | 2000