The Performance Analysis of an Improved Robust Beamforming Algorithm

The conventional minimum variance beamformer (MVB) is known to have better resolution and much better interference rejection capability when the array steering vector is accurately known. However, the major problem of the MVB is that it lacks robustness against the unknown array steering vector errors. Therefore, diagonal loading and its extended versions have been a popular approach to improve the robustness of MVB. In this paper, we provide an improved robust MVB (RMVB) algorithm, which has been derived by enforcing a spherical uncertainty set constraint on the array steering vector. The new algorithm has reasonable computation complexity and more numerical stability. Simulation results show that the new algorithm is effective in the presence of array steering vector errors.