Name
Abstract | ||
|---|---|---|
We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be expressed using a full-rank dimension reducing transform, providing new results for data-adaptive dimensionality reduction. We consider Krylov subspace methods computed with the Powers-of-R (PoR) and Conjugate Gradient (CG) techniques, illustrating how a fast CG-based algorithm can be formed by beneficially exploiting that the CG-algorithm diagonalizes the reduced-dimension covariance. Our simulations show the benefits of the proposed approaches. |
Year | Venue | Field |
|---|---|---|
2014 | arXiv: Information Theory | Krylov subspace,Conjugate gradient method,Mathematical optimization,Dimensionality reduction,Beamforming algorithm,Capon,Mathematics,Covariance |
DocType | Volume | Citations |
Journal | abs/1402.5691 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Somasundaram | 1 | 0 | 1.35 |
| P. Li | 2 | 214 | 28.84 |
| Nigel H. Parsons | 3 | 29 | 1.84 |
| Rodrigo Caiado de Lamare | 4 | 0 | 2.03 |