Robust Adaptive Beamforming using nonlinear shrinkage method

In the scenario that the number of sensors and the number of samples have the same order of magnitude, the conventional adaptive beamformers fail to provide good performance, due to the ill-condition of the sample covariance matrix. Using Random Matrix Theory (RMT) which can give a good estimation of the covariance matrix, we proposed a robust adaptive beamformer. we makes no assumption on the data distribution, and it is totally parameter free. The performance of our method is shown in the numerical simulations and compared with other robust adaptive beamformers. The results show that our method is robust against errors in the steering vector and small size of samples.