Robust adaptive beamforming with random steering vector mismatch

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

23 Scopus Citations
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Author(s)

  • Bin Liao
  • Chongtao Guo
  • Lei Huang
  • Qiang Li
  • Guisheng Liao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)190-194
Journal / PublicationSignal Processing
Volume129
Online published5 Jun 2016
Publication statusPublished - Dec 2016

Abstract

In this paper, random steering vector mismatches in sensor arrays are considered and probability constraints are imposed for designing a robust minimum variance beamformer (RMVB). To solve the resultant design problem, a Bernstein-type inequality for stochastic processes of quadratic forms of Gaussian variables is employed to transform the probabilistic constraint to a deterministic form. With the use of convex optimization techniques, the deterministic problem is reformulated to a semidefinite programming (SDP) problem which can be efficiently solved. In order to overcome the degradation caused by the presence of the signal-of-interest (SOI) in the training snapshots, two methods with different application conditions to interference-plus-noise covariance matrix (INCM) construction are also introduced. Additionally, the uncertainty of the sample covariance matrix is taken into account to improve the robustness when the INCM-based approaches are not feasible. Numerical examples are presented to demonstrate the performances of the proposed robust beamformers in different scenarios.

Research Area(s)

  • Robust minimum variance beamforming, Semidefinite programming, Steering vector mismatch

Citation Format(s)

Robust adaptive beamforming with random steering vector mismatch. / Liao, Bin; Guo, Chongtao; Huang, Lei; Li, Qiang; Liao, Guisheng; So, H. C.

In: Signal Processing, Vol. 129, 12.2016, p. 190-194.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review