Reduced-dimension robust capon beamforming using Krylov-subspace techniques


We have a tendency to gift low-complexity, quickly converging strong adaptive beamformers, for beamforming large arrays in snapshot deficient scenarios. The proposed algorithms are derived by combining information-dependent Krylov-subspace-primarily based dimensionality reduction, using the Powers-of-R or conjugate gradient (CG) techniques, with ellipsoidal uncertainty set based mostly strong Capon beamformer ways. Additional, we give a detailed computational complexity analysis and contemplate the efficient implementation of automatic, on-line dimension-selection rules. We tend to illustrate the advantages of the proposed approaches using simulated data.

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