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This paper proposes an iterative robust capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector based on conventional RCB formulation. Instead of using a fixed uncertainty level, we adaptively update the uncertainty level at each iteration. The uncertainty is updated based on the projection of the presumed steering vector onto the noise...
Many advanced adaptive beamformers are robust against arbitrary array steering vector (ASV) mismatches within a presumed uncertainty set. Adaptive array tolerating significant steering direction error usually requires a large size of ASV uncertainty set. In such case, however, the output signal-to-interference-plus-noise ratios (SINRs) of robust methods degrade quickly with the increasing size of...
We propose a framework based on the use of a set of beampattern shaping constraints. The proposed beamformer possesses not only adaptive interference-rejection ability and robustness against large steering direction errors, but also direct sidelobe control. It contains a new weighing ratio that can automatically control the relative proportions of interference-rejection and sidelobe suppression in...
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