Robust Adaptive Beamforming in Conditions of Sparse Corruption Array Element

Here a novel robust adaptive beamformer, which can avoid the performance degradation in conditions of sparse corruption array element, based on matrix completion theory and matrix reconstruction is proposed. The matrix completion theory and a two-fold Hankel structure are utilized to recover the uncompleted data matrix precisely. For eliminating the effect of presence of desired signal, the reconstruction of interference-plus-noise covariance matrix is adopted. Meanwhile, the estimation of the desired signal steering vector is obtained by utilizing the relationship between the principal eigenvector of the reconstructed desired signal covariance matrix and the desired signal steering vector. The superior robustness of the novel beamformer in conditions of sparse corruption array element is demonstrated in the simulation.