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 useful 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 yields a diagonal reduced-dimension covariance matrix. Our simulations show the benefits of the proposed approaches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638442 | ICASSP |
Keywords | Field | DocType |
array signal processing,conjugate gradient methods,PoR computation,conjugate gradient technique,data adaptive Capon beamforming,data adaptive Krylov subspace dimensionality reduction technique,data adaptive dimensionality reduction,full-rank dimension reducing transform,powers-of-R computation,reduced dimension Capon beamforming,robust Capon beamforming,Krylov subspace methods,Robust adaptive beamforming,dimensionality reduction | Diagonal,Conjugate gradient method,Krylov subspace,Beamforming,Mathematical optimization,Dimensionality reduction,Computer science,Capon,Covariance matrix,Conjugate residual method | Conference |
ISSN | Citations | PageRank |
1520-6149 | 4 | 0.42 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Dilshan Somasundaram | 1 | 47 | 4.66 |
| Nigel H. Parsons | 2 | 29 | 1.84 |
| Peng Li | 3 | 9 | 1.53 |
| Rodrigo C. de Lamare | 4 | 1461 | 179.59 |