Robust Adaptive Beamforming with Proximal Method

Abstract

This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers for large arrays. The method uses proximal gradient descent along with Nesterov’s acceleration. It has ${\mathcal{O}}\left( {{N^2}} \right)$ computational complexity per iteration where N is the array size. For sparse linearly constrained adaptive beamforming, the proposed method achieves performances comparable to the conjugate gradient method. For sparse robust adaptive beamforming with conic constraints, the proposed method is much more efficient than the standard interior point solver.