Iterative Robust Minimum Variance Beamforming

TLDR

Worst‑case performance optimization leads to adaptive beamformers that use an uncertainty set of the desired array steering vector for robustness against mismatches, but large mismatches require expanding this set, weakening interference‑plus‑noise suppression and degrading output SINR. The study proposes an iterative robust minimum variance beamformer (IRMVB) that iteratively searches for the desired array steering vector using a small uncertainty sphere and flat ellipsoid. The IRMVB achieves this by iteratively applying the small uncertainty sphere and flat ellipsoid to locate the steering vector, thereby maintaining interference‑plus‑noise suppression. The IRMVB preserves interference‑plus‑noise suppression and achieves higher output SINR, as demonstrated by theoretical analysis and simulations.

Abstract

Based on worst-case performance optimization, the recently developed adaptive beamformers utilize the uncertainty set of the desired array steering vector to achieve robustness against steering vector mismatches. In the presence of large steering vector mismatches, the uncertainty set has to expand to accommodate the increased error. This degrades the output signal-to-interference-plus-noise ratios (SINRs) of these beamformers since their interference-plus-noise suppression abilities are weakened. In this paper, an iterative robust minimum variance beamformer (IRMVB) is proposed which uses a small uncertainty sphere (and a small flat ellipsoid) to search for the desired array steering vector iteratively. This preserves the interference-plus-noise suppression ability of the proposed beamformer and results in a higher output SINR. Theoretical analysis and simulation results are presented to show the effectiveness of the proposed beamformer.