A Low Complexity High Performance Robust Adaptive Beamforming

In this paper, we propose a low complexity adaptive beamforming with joint robustness against array steering vector (ASV) mismatch and array manifold errors. The proposed robust beamforming is based on the diagonal loading technique, and the diagonal loading factor can be adjusted adaptively according to the input signal-to-noise ratios (SNRs). The eigenvalue corresponding to the desired signal is identified by projecting the presumed ASV of the desired signal onto the eigenvectors of the sample covariance matrix. We find that the ratio of the eigenvalues corresponding to the desired signal and the noise can be used to accurately reflect the input SNR when the input SNRs is large enough, and the ratio is also the diagonal loading factor. Then, an orthogonal subspace is obtained with the steering vectors associated with the region where the desired signal may locate at. Also, the actual steering vector is estimated as a linear combination of this orthogonal subspace. In order to further reduce the computation complexity, we use the traditional diagonal loading method in low SNRs. Simulations results demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches, and the complexity is significantly reduced compared with popular existing methods.