Robust adaptive beamforming based on matched spectrum processing with little prior information
Yong Chen, Fang Wang, Jianwei Wan, Ke Xu · 2016
Robust adaptive beamforming (RAB) has became a popular research topic, with various RAB techniques being proposed in the past decades. However, because the sample covariance matrix rather than the interference-plus-noise co-variance matrix is used to calculate the weight vector, the performance of the previously developed RAB techniques is not optimal. In this paper, a novel RAB algorithm, which uses the reconstructed interference-plus-noise covariance matrix, is developed. First, the interference-plus-noise covariance matrix and the desired signal covariance matrix are reconstructed by matched spectrum processing. Then, the weight vector of RAB is directly obtained using the general-rank minimum variance distortionless response method. A significant advantage of the proposed RAB is that only little prior information is required. The imprecise knowledge of the antenna array geometry and the angular sectors (in which the desired signal and interferences are located) is sufficient for the proposed RAB algorithm. Simulation results demonstrate that the proposed method outperforms other previously developed RAB techniques.