Robust Design for signal mismatch with Steering Vector and Covariance Matrix Constraints

This paper is concerned with a new robust design algorithm for signal mismatch, the modified steering vector and covariance matrix are investigated. Under the correlation coefficient and norm constraints, the mismatched desired signal steering vector is estimated via output power maximization. The original design is nonconvex, and usually Np-hard. Then the relaxed semidefinite programming (SDP) is employed to solve the problem, and the relaxation is tight via analytically proven. Subsequently, the sample covariance matrix is further restructured with the shrinkage method and the subspace theorem based on the corrected steering vector in order to remove the desired signal. Finally, we provide some numerical results to show that the proposed method gives satisfactory results under different scenarios.