Robust direction-adaptive based reduced-rank beamforming algorithm

A robust reduced-rank adaptive beamforming method based on the joint iterative optimization direction-adaptive (JIO-DA) scheme is developed for the large array scenarios based on the linear constraint minimum variance (L-CMV) criterion. The JIO-DA scheme jointly optimizes a reduced-rank filter and a transforming matrix. The columns of the transforming matrix are considered as direction vectors and are updated one by one. Besides, a new steering vector is generated by rotating the steering vector to increase the robustness against pointing error. The recursive least-squares (RLS) algorithm is then developed to update these direction vectors and the reduced-rank filter. Simulation results show that the proposed scheme improves the convergence rate of beamforming with increasing robustness against pointing error.