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.
Reduced-dimension robust capon beamforming using Krylov-subspace techniques
IEEE Transactions on Aerospace and Electronic Systems ; 51 , 1 ; 270-289
2015-01-01
1558115 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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