We present low-complexity, quickly converging robust adaptive beamformers, for beamforming large arrays in snapshot deficient scenarios. The proposed algorithms are derived by combining data-dependent Krylov-subspace-based dimensionality reduction, using the Powers-of-R or conjugate gradient (CG) techniques, with ellipsoidal uncertainty set based robust Capon beamformer methods. Further, we provide a detailed computational complexity analysis and consider the efficient implementation of automatic, online dimension-selection rules. We illustrate the benefits of the proposed approaches using simulated data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Reduced-dimension robust capon beamforming using Krylov-subspace techniques


    Beteiligte:


    Erscheinungsdatum :

    2015-01-01


    Format / Umfang :

    1558115 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Robust Capon Beamformers for Wideband Acoustic Imaging

    Wang, Zhisong / Li, Jian / Nishida, Toshikazu et al. | AIAA | 2003


    USE OF ROBUST CAPON BEAMFORMER FOR EXTRACTING AUDIO SIGNALS

    Bao, Chaoying / Pan, Jie / Jia, Ling | TIBKAT | 2020


    Multigrid Preconditioning of Krylov Subspace Methods for CFD Applications

    Smith, J. / Celik, I. / AIAA | British Library Conference Proceedings | 2000