Iterative robust Capon beamforming

• We propose a closed-form expression of covariance fitting based RCB.
• We develop two novel efficient iterative robust Capon beamformers.
• We prove the iterative convergence properties and discuss on other properties.
• The two proposed beamformers perform stably through a wide initial value.
• Pointing error, calibration error, and array element position error are simulated.

Signal steering vector (SV) error can cause desired signal cancellation in adaptive beamforming. A covariance fitting based robust Capon beamforming (CFRCB) has been developed to solve this problem. Such a solution cannot be expressed in a closed form and its performance is highly affected by the initial value of SV error norm bound. In this paper, we propose an approximate closed-form expression of CFRCB, then develop two novel beamformers based on iterative implementation of this closed-form expression. Theoretical analysis and simulation results indicate that these beamformers improve in performance with every iterative step and converge to a stabilized solution. In addition, they perform well through a wide range of initial SV errors norm bound range, are easily implemented and computationally efficient. We also present a number of numerical examples comparing the proposed beamformers with similar classical beamformers.