Robust Adaptive Beamforming Using a Fully Data-dependent Loading Technique

This paper deals with adaptive array beamforming in the presence of errors due to steering vector mismatch and finite sample effect. Diagonal loading (DL) is one of the widely used techniques for dealing with these errors. However, the main drawback of DL techniques is that there is not an easy and reliable manner to determine the required loading factor. Recently, serval DL approaches proposed the so-called automatic scheme on computing the required loading factor. In this paper, we propose a fully data-dependent loading to overcome the difficulties. The novelty is that the proposed method does not require any additional sophisticated scheme to choose the required loading. The loading factor can be completely obtained from the received array data. Analytical formulas for evaluating the performance of the proposed method under random steering vector error are further derived. Simulation results are provided to confirm the validity of the proposed method and make comparison with the existing DL methods.