Robust adaptive beamforming based on the direct biconvex optimization modeling

In general, robust adaptive beamforming (RAB) is modeled as a nonconvex optimization problem. The most of existing methods solve it indirectly by approximating the nonconvex problem to the convex optimization problem, which will cause the approximation error. Different from the existing methods, a novel method, which reformulates RAB as the biconvex form directly by use of an auxiliary variable, is proposed. Thus, the errors caused by the approximation can be avoided and the performance will be improved. Then the alternating direction methods of multiplier (ADMM) algorithm is applied to solve it. Compared with the existing methods, the results of the simulation experiments indicated that the proposed method has better performance encountered with types of signal steering vector mismatches.