Review of user parameter-free robust adaptive beamforming algorithms

  • Authors:

  • Lin Du;Tarik Yardibi;Jian Li;Petre Stoica

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116130, Gainesville, FL 32611-6130, USA;Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116130, Gainesville, FL 32611-6130, USA;Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116130, Gainesville, FL 32611-6130, USA;Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2009

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Abstract

This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms. We present the ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, and the convex combination (CC) as well as the general linear combination (GLC) approaches. The purpose of these methods is to mitigate the effect of small sample size and steering vector errors on the standard Capon beamformer (SCB). We also present sparsity based iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (referred to as IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning), which exploit sparsity to estimate the signal parameters. We provide a thorough evaluation of these beamforming methods in terms of power and spatial spectrum estimation accuracies, output signal-to-interference-plus-noise ratio (SINR) and resolution under various scenarios including coherent, non-coherent and distributed sources, steering vector mismatches, snapshot limitations and low signal-to-noise ratio (SNR) levels. Furthermore, we discuss the computational complexities of the algorithms and provide insights into which algorithm is the best choice under which circumstances.