New convex approaches to general MVDR robust adaptive beamforming problems

Abstract Consider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both desired signal steering vector and interference‐plus‐noise covariance (INC) matrix. The beamformer design problem is an array output power maximization problem, subject to three constraints vector, namely, a (convex or nonconvex) quadratic constraint ensuring that direction‐of‐arrival (DOA) of separated from DOA region all linear combinations interference vectors, double‐sided norm constraint, similarity constraint; as well ball INC matrix, which centered at given data sample To tackle nonconvex new tightened semidefinite relaxation (SDR) approach proposed globally solution; otherwise, sequential convex approximation (SCA) method established return locally solution. simulation results show MVDR beamformers matrix have better performance (in terms of, e.g., signal‐to‐interference‐plus‐noise ratio) than existing by only.