Robust adaptive beamforming using probability-constrained optimization

Recently, robust minimum variance (MV) beamforming which optimizes the worst-case performance has been proposed in S.A. Vorobyov et al. (2003), R.G. Lorenz and S.P. Boyd (2005). 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