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To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation that uses an uncertainty sphere to model the...
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...
It is well known that the performance of adaptive beamformers may degrade in the presence of steering errors. In this context, diagonal loading is one of the most popular methods used for robust beamforming, and can be derived from an l2 norm constraint. Equivalently, this method assumes a white Gaussian prior on the beamforming vector, similar to ridge regression in statistical point of view. By...
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