2015
DOI: 10.1109/taes.2014.130485
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Reduced-dimension robust capon beamforming using Krylov-subspace techniques

Abstract: 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 dimen… Show more

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Cited by 76 publications
(71 citation statements)
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“…Since we have chosen σ 2 1,KF to be equal to the DAS estimate, it means that the spatial spectrum would be similar to the DAS spectrum. We apply the ad hoc rescaling of the weights (29) noting that this will not affect the output SNR. In the following, we term this implementation the Kalman Filtering Implementation of WC-RAB (WC-KF).…”
Section: Worst-case Robust Adaptive Beamformer Using Kalman Filtementioning
confidence: 99%
See 3 more Smart Citations
“…Since we have chosen σ 2 1,KF to be equal to the DAS estimate, it means that the spatial spectrum would be similar to the DAS spectrum. We apply the ad hoc rescaling of the weights (29) noting that this will not affect the output SNR. In the following, we term this implementation the Kalman Filtering Implementation of WC-RAB (WC-KF).…”
Section: Worst-case Robust Adaptive Beamformer Using Kalman Filtementioning
confidence: 99%
“…THE KRYLOV-RDRCB In [29], a family of Krylov-subspace reduced-dimension robust Capon beamformers (Krylov-RDRCBs) was derived, which project the data onto a reduced-rank Krylov subspace and solve an RCB problem in the reduced-dimension space. Here, we describe the Conjugate Gradient RDRCB, which uses the conjugate algorithm [42], [43] to expand a rank-N Krylov-subspace basis…”
Section: Worst-case Robust Adaptive Beamformer Using Kalman Filtementioning
confidence: 99%
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“…However, because the microphone spacing is generally greater than the voice signal wavelength, with the classic adaptive beamformer based on GSC, like the Griffiths-Jim beamformer [9] and some others, slight direction estimation error will cancel part of the desired signal [10]. Many signal processing techniques, which are called robust adaptive beamforming, have been proposed to avoid the cancellation of the desired signal [11][12][13][14][15], because their performance is robust against errors. However, the problems of using GSC algorithm are not so serious while processing SLF signals.…”
Section: Introductionmentioning
confidence: 99%