The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we will first provide a complete analysis of a norm constrained Capon beamformer (NCCB), which uses a norm constraint on the weight vector to improve the robustness against array steering vector errors and noise. Our analysis is thorough and sheds more light on the choice of the norm constraint than what was commonly known. We also briefly discuss our recently proposed robust Capon beamformer (RCB), which is obtained via extending the covariance matrix fitting approach to the case of uncertain steering vectors. Our RCB is based on a clear theoretical background and explicitly addresses the steering vector uncertainty problems. Both NCCB and RCB can be efficiently computed at a comparable cost with that of SCB. Performance comparisons of NCCB and RCB via a number of numerical examples are also presented.
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