Robust adaptive beamforming algorithm under nonlinear constraint

Abstract

When the array steering vector is precisely known, adaptive beamforming is known to have resolution and interference rejection capability. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the assumed array response and the true array response. Similar types of degradation can occur when the signal array response is known exactly, but the training sample size is small. In this paper, we propose a novel robust adaptive beamforming algorithm, which is based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix. The proposed algorithm belongs to the class of diagonal loading approaches, but the diagonal loading term can be precisely calculated, which is incorporated at each step. To decrease computation complexity, the weight vector based on the variable diagonal loading is obtained by Taylor series. The proposed algorithm has nearly optimal performance under good conditions, provides the robustness against the signal steering vector mismatches and the small training sample size, and makes the mean output array SINR consistently close to the optimal one. Simulation results validate substantial performance improvements relative to other adaptive beamforming methods.