Robust adaptive beamforming based on sampling covariance matrix reconstruction and steering vector estimation

Abstract

Traditional adaptive beamforming algorithms require high accuracy for steering vectors(SV), array models and desired signal(DS). However, the performance of the beamformer will seriously degrade when the DS is present in training snapshots. For the purpose of improving output performance of adaptive beamformer, a novel adaptive beamforming algorithm is proposed. This approach estimates the desired signal SV and reconstructs the sampling covariance matrix (CM) based on integrating over a undesired signal region. Furthermore, only a little prior knowledge is required, such as the approximate incident angle of the DS. The proposed algorithm remove not only the influence of the DS in the sampling covariance matrix, but also the effect of background noise perturbation, which is significantly improved compared with other methods. The results of data simulation experiments confirms that the beamformer has a excellent performance in output performance.