Iterative doubly constrained robust capon beamformer using adaptive uncertainty level

In recent years, some iterative robust beamforming (RAB) algorithms using a fixed adaptive uncertainty level (Fu-IRAB) are proposed to overcome the SINR performance deterioration of the conventional RAB methods in the presence of large array steering vector (ASV) errors. However, more iteration steps are consumed before the actual steering vector is reached. This paper introduces a new adaptive beamformer based on the traditional DCRCB (doubly constrained robust capon beamformer using adaptive uncertainty level), by means of updating the uncertainty level adaptively that is proportional to the amount of the mismatch in each step, resulting in a faster convergence rate in comparison with the Fu-IRAB. In addition, a new robustness is provided to against the initial uncertainty level in the first iteration.