Low-complexity implementation for worst-case optimization-based robust adaptive beamforming

In this paper, an efficient low-complexity robust adaptive beamforming method based on worst-case performance optimization is proposed. Lagrangian method was applied to obtain the expression for the robust adaptive weight vector, which is optimized on the boundary of the steering vector uncertainty region, that is to say, in the worst mismatch case. Combining the constraint condition and the eigendecomposition of the array covariance matrix, root-finding method is used to obtain the optimal Lagrange multiplier. Then, the diagonal loading-like robust weight vector is achieved. The implementation efficiency is greatly improved since the main computational burden is the eigendecomposition operator. Numerical results show that the performance of the proposed method is nearly identical to the robust Capon beamforming.