Data-adaptive reduced-dimension robust Capon beamforming


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.

DOI: 10.1109/ICASSP.2013.6638442

3 Figures and Tables

Cite this paper

@article{Somasundaram2013DataadaptiveRR, title={Data-adaptive reduced-dimension robust Capon beamforming}, author={Samuel Dilshan Somasundaram and Nigel H. Parsons and Peng Li and Rodrigo C. de Lamare}, journal={2013 IEEE International Conference on Acoustics, Speech and Signal Processing}, year={2013}, pages={4159-4163} }