Robust adaptive beamforming based on iterative implementation of worst-case performance optimisation

In the presence of large pointing error, an iterative worst-case performance optimisation based robust beamformer is proposed in a new perspective. Based on the relationship between the optimal weight vector and the steering vector (SV) of the Capon beamformer, the proposed approach can update the estimated desired SV through iterative computations with a small uncertainty set, and does not need any reformulation of the optimisation problem. Simulation results show that the proposed iterative approach outperforms the beamformers with large uncertainty set in the presence of large pointing error.