Reduced-Dimension Robust Capon Beamforming Using Krylov-Subspace Techniques

被引：40

作者：

Somasundaram, Samuel D. ^{[1
]}

Parsons, Nigel H. ^{[1
]}

Li, Peng ^{[2
,4
]}

De Lamare, Rodrigo C. ^{[3
,4
]}

机构：

[1] Thales UK Ltd, Gen Sonar Studies, Stockport SK3 0XB, Cheshire, England

[2] Pontificia Univ Catolica Rio de Janeiro, Rio de Janeiro, Brazil

[3] Pontificia Univ Catolica Rio de Janeiro, Ctr Telecommun Studies CETUC, Rio de Janeiro, Brazil

[4] Univ York, Dept Elect, Commun Res Grp, York YO10 5DD, N Yorkshire, England

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2015年 / 51卷 / 01期

关键词：

CONJUGATE-GRADIENT TECHNIQUES; INTERFERENCE SUPPRESSION; DERIVATIVE CONSTRAINTS; PARAMETER-ESTIMATION; ALGORITHMS; PROJECTION; EQUALIZATION; PERFORMANCE;

D O I：

10.1109/TAES.2014.130485

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

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.

引用

页码：270 / 289

页数：20

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