A class of constrained adaptive beamforming algorithms based on uniform linear arrays

Authors:
Lei Zhang;Wei Liu;Richard J. Langley
Affiliations:
Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK;Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK;Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 9
Cited 3

Adaptive signal processing

Adaptive signal processing
Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Adaptive wideband beamforming with sensor delay-lines

Signal Processing
Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis

Signal Processing
Wideband Beamforming: Concepts and Techniques

Wideband Beamforming: Concepts and Techniques
An iterative algorithm for the computation of the MVDR filter

IEEE Transactions on Signal Processing
A Competitive Mean-Squared Error Approach to Beamforming

IEEE Transactions on Signal Processing
Eigenvalue Beamforming Using a Multirank MVDR Beamformer and Subspace Selection

IEEE Transactions on Signal Processing
Low-complexity variable step-size mechanisms for stochastic gradient algorithms in minimum variance CDMA receivers

IEEE Transactions on Signal Processing - Part I

Robust forward backward based beamformer for a general-rank signal model with real-valued implementation

Signal Processing
Generalized eigenvector problem for Hermitian Toeplitz matrices and its application to beamforming

Signal Processing
Robust adaptive beamforming using an iterative FFT algorithm

Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

A new class of adaptive beamforming algorithms is proposed based on a uniformly spaced linear array by constraining its weight vector to a specific conjugate symmetric form. The method is applied to the well-known reference signal based (RSB) beamformer and the linearly constrained minimum variance (LCMV) beamformer as two implementation examples. The effect of the additional constraint is equivalent to adding a second step in the derived adaptive algorithm. However, a difference arises for the RSB case since no direction-of-arrival (DOA) information of the desired signal is available, which leads to a two-stage structure for incorporating the imposed constraint. Compared to the traditional algorithms, the proposed ones can achieve a faster convergence speed and a higher steady state output signal-to-interference-plus-noise ratio, given the same stepsize.