Robust adaptive beamforming based on convex optimization

Adaptive beamforming is an efficient way of spatial filtering in the presence of interference and noise. However, the conventional adaptive beamforming, e.g., MVDR, may degrade significantly due to the poorly estimated covariance matrix or steering vector errors. Convex optimization has now emerged as a major signal processing tool and made a significant impact on numerous problems because of its foundational nature and potential ability in signal processing. Thus in this paper, several robust adaptive beamforming algorithms based on the convex optimization are presented and evaluated, where a novel mathematical tool called “Yalmip” is used to solve the second-order cone problem in these algorithms. Numerical simulations and comparison show that these robust adaptive beamforming algorithms still approach robust to the above-mentioned problems.