Data-Adaptive Reduced-Dimension Robust Beamforming Algorithms
| Title: | Data-Adaptive Reduced-Dimension Robust Beamforming Algorithms |
|---|---|
| Authors: | Somasundaram, S., Li, P., Parsons, N., de Lamare, R. C. |
| Publication Year: | 2014 |
| Collection: | Computer Science Mathematics |
| Subject Terms: | Computer Science - Information Theory, Computer Science - Systems and Control |
| More Details: | 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. Comment: 5 pages, 2 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/1402.5691 |
| Accession Number: | edsarx.1402.5691 |
| Database: | arXiv |
| Description not available. |