Adaptive beamforming based on covariance matrix reconstruction by exploiting interferences' cyclostationarity

Abstract

Adaptive beamforming is known to be sensitive to array system mismatch, especially when the sample covariance matrix is used and the desired signal is present in the training snapshot. To alleviate the sensitivity, in this paper, the sample covariance matrix is replaced by the interference-plus-noise covariance matrix (INCM), which is reconstructed by exploiting the cyclostationarity of interference signals. In contrast to the existing INCM reconstruction methods, the proposed technique is based on the knowledge of the interferences' cycle frequencies and needs no information of the array structure, thus it can deal with unknown perturbations in the array. The numerical simulations show that the proposed method improves the robustness of adaptive beamformers and has superior performance to the existing INCM reconstruction methods especially for strong interferences.