Robust adaptive beamforming in impulsive noise environments with unknown statistics
Ting Shu, X. L. Liu · 2009
In this paper, a new method is proposed for the problem of adaptive beamforming in non-Gaussian impulsive noise environments. The proposed method normalizes each spatial snapshot vector by its Euclidian norm, so that the conventional second-order statistics-based beamformers can become applicable to the non-Gaussian heavy-tailed noise environments. The performance of the proposed method is studied through simulations. The results show that the proposed method perform highly reliably in the case of non-Gaussian noise and near optimally if the noise is Gaussian.