The minimum power distortionless response beamformer has a good interference rejection capability, but the\ndesired signal will be suppressed if signal steering vector or data covariance matrix is not precise. The worst-case\nperformance optimization-based robust adaptive beamformer (WCB) has been developed to solve this problem.\nHowever, the solution of WCB cannot be expressed in a closed form, and its performance is affected by a prior\nparameter, which is the steering vector error norm bound of the desired signal. In this paper, we derive an\napproximate diagonal loading expression of WCB. This expression reveals a feedback loop relationship between\nsteering vector and weight vector. Then, a novel robust adaptive beamformer is developed based on the iterative\nimplementation of this feedback loop. Theoretical analysis indicates that as the iterative step increases, the\nperformance of the proposed beamformer gets better and the iteration converges. Furthermore, the proposed\nbeamformer does not subject to the steering vector error norm bound constraint. Simulation examples show that the\nproposed beamformer has better performance than some classical and similar beamformers.
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