Review of User Parameter-Free Robust Adaptive Beamforming Algorithms

This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms, including ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, the shrinkage based approaches, and iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning). The purpose of these algorithms is to mitigate the negative effects of model errors on the standard Capon beamformer (SCB). We provide a thorough evaluation or these methods under various scenarios and give insights into which algorithm is the best choice under which circumstances.