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

Hongya Ge, Ivars P. Kirsteins, Louis L. Scharf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesIV1001-IV1004
StatePublished - 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period5/14/065/19/06

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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