On low complexity robust beamforming with positive semidefinite constraints

This paper addresses the problem of robust beamforming for general-rank signal models with norm bounded uncertainties in the desired and received signal covariance matrices as well as positive semidefinite constraints on the covariance matrices. Two novel minimum variance robust beamformers are derived in closed-form. The first one basically is the closed-form version of an existing iterative algorithm, while the second one offers even better performance with respect to the first one. Both of them have the advantage of low complexity. The effectiveness and performance improvement of the proposed beamformers are verified by simulation results.