Robust adaptive beamforming against large steering vector mismatch using multiple uncertainty sets

Many efforts have been recently devoted to adaptive beamformers using one uncertainty set to achieve robustness against steering vector mismatch. However, these robust adaptive beamformers based on worst-case performance optimization suffer from difficulty in selecting an appropriate size of the uncertainty set. Besides, their performances degrade dramatically if large steering vector mismatch occurs. This motivates us to develop a robust beamforming approach to handle the large uncertainty set problem. In this paper, we utilize multiple small uncertainty sets, instead of using a single large set, to cover the whole large uncertainty region. Two efficient algorithms, iterative semidefinite programming-based robust adaptive beamforming (ISDP-RAB) and iterative linearization-based robust beamforming (IL-RAB), are developed to solve the nonconvex problem. Simulation results indicate that the proposed method offers a significant performance improvement in case of large steering vector mismatch.