Robust direction-adaptive based reduced-rank beamforming algorithm

摘要

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 reducedrank filter. Simulation results show that the proposed scheme improves the convergence rate of beamforming with increasing robustness against pointing error.