Robust adaptive beamforming based on jointly estimating covariance matrix and steering vector

Yujie Gu*, Amir Leshem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

In this paper, a new adaptive beamforming algorithm with joint robustness against covariance matrix uncertainty as well as steering vector mismatch is proposed. First, the theoretical covariance matrix is estimated based on the shrinkage method. Subsequently, the difference between the actual and the presumed steering vector is estimated by solving a quadratic convex optimization problem, which enables correction of the presumed steering vector. Unlike other robust beamforming techniques, neither the norm of the steering vector nor the upper bound of the norm of the mismatch vector is assumed in our approach. Simulation results show the effectiveness of the proposed algorithm both in terms of output performance and computational complexity.

Original languageAmerican English
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2640-2643
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

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

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Adaptive beamforming
  • quadratic programming
  • robustness
  • shrinkage estimation

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