Robust adaptive beamforming via estimating steering vector based on semidefinite relaxation

Most of the known robust adaptive beamforming techniques can be unified under one framework. This is to use minimum variance distortionless response principle for beamforming vector computation in tandem with sample covariance matrix estimation and steering vector estimation based on some information about steering vector prior. Motivated by such unified framework, we develop a new robust adaptive beamforming method based on finding a more accurate estimate of the actual steering vector than the available prior. The objective for finding such steering vector estimate is the maximization of the beamformer output power under the constraints that the estimate does not converge to an interference steering vector and does not change the norm of the prior. The resulting optimization problem is a non-convex quadratically constrained quadratic programming problem, which is NP hard in general, but can be efficiently and exactly solved in our specific case. Our simulation results demonstrate the superiority of the proposed method over other robust adaptive beamforming methods.