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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...
Two new approaches to adaptive beamforming in sparse subarray-based sensor arrays are proposed. Each subarray is assumed to be well calibrated but the intersubarray gain and/or phase mismatches are assumed to remain unknown or imperfectly known. Our first approach is based on a worst-case beamformer design that, unlike the existing worst-case designs, exploits a structured ellipsoidal uncertainty...
Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed. Each subarray is assumed to be well calibrated, so that the steering vectors of all subarrays are exactly known. However, the intersubarray gain and/or phase mismatches are known imperfectly or remain completely unknown. Our first approach is based on a worst-case beamformer design which,...
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