SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Robust Adaptive Beamforming Using Probability-Constrained Optimization

Author(s)

Sergiy Vorobyov	Darmstadt University of Technology
Yue Rong	Darmstadt University of Technology
Alex Gershman	Darmstadt University of Technology

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Abstract

Recently, robust minimum variance (MV) beamforming which optimizes the worst-case performance has been proposed in [1], [2]. The worst-case approach, however, might be overly conservative in practical applications. In this paper, we propose a more flexible approach that formulates the robust adaptive beamforming problem as a probability-constrained optimization problem with homogeneous quadratic cost function. Unlike the general probability-constrained problem which can be nonconvex and NP-hard, our problem can be reformulated as a convex nonlinear programming (NLP) problem, and efficiently solved using interior-point methods. Simulation results show an improved robustness of the proposed beamformer as compared to the existing state-of-the-art robust adaptive beamforming techniques.