Robust adaptive beamforming based on the effectiveness of reconstruction

The beamformers by joint estimation of the steering vector (SV) and the reconstructed interference-plus-noise covariance (INC) matrix have been investigated. However, few of them concerned about the effectiveness of reconstruction. In this paper, a novel beamformer is proposed, which introduces the improve factor (IF) to evaluate the improvement scale of output signal-to-interference-plus-noise ratio (SINR) with and without reconstruction. By exploiting the relationship between the IF and input signal-to-noise ratio (SNR), it can be made a trade off between the extent of the performance improved and computational load that reconstruction required. Therefore, the SV is estimated at first, and then the level of SNR determines whether the INC matrix is required to reconstruct. During the implementation, a spherical uncertainty set and the sparsity of interference direction are used. Simulation results indicate that the proposed beamformer has better performance compared with existing adaptive beamforming algorithms.