Data dimension reduction using krylov subspaces: Making adaptive beamformers robust to model order-determination

Document Type

Conference Proceeding

Publication Date

12-1-2006

Abstract

In this work, we present a class of low-complexity reduced-dimension adaptive beamformers constructed from expanding Krylov subspaces. We demonstrate how the data dimensionality reduction obtained from Krylov pre-processing decreases the sensitivity of reduced-rank adaptive beamforming techniques to incorrect model-order selection and lessens the computational complexity of systems involving large arrays with many elements. An important advantage of the proposed dimensionality reduction scheme is that it relieves reduced-rank methods from the stringent requirement on the precise model order determination. © 2006 IEEE.

Identifier

33947651033 (Scopus)

ISBN

[142440469X, 9781424404698]

Publication Title

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

ISSN

15206149

First Page

IV1001

Last Page

IV1004

Volume

4

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