Robust Adaptive Beamforming Based on Super-Gaussian Loading and Its Performance Analysis

In order to solve the problem of beamformers performance degradation caused by signal steering vector and sample covariance matrix mismatch errors, a robust adaptive beamforming algorithm based on Super-Gaussian Loading (SGL) is put forward in this paper. By correcting these two error uncertainties together through lp norm, the proposed algorithm overcomes the drawback inl2 norm issue that cannot optimally calibrate the two errors at the same time. The optimalis obtained through genetic algorithm, and the better output performance can be got comparing withlp norm approach in different experiment conditions. The Super-Gaussian Loading algorithm transforms the complex modeling for two uncertainties into norm p optimization problem, and thus gets better result than standard diagonal loading method.