Low-Complexity Robust Adaptive Beamforming Based on Shrinkage and Cross-Correlation

In this paper, we propose a low-complexity robust adaptive beamforming (RAB) technique based on shrinkage and cross-correlation methods. We firstly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) method which estimates the desired signal steering vector mismatch and the desired signal power. We then develop low-cost stochastic gradient recursions to estimate the INC matrix and update the beamforming weights, rather than directly computing the beamforming weights with matrix inversions as in LOCSME, resulting in the proposed LOCSME-SG algorithm. Simulations show that LOCSME-SG achieves excellent output signal-to-interference-plus-noise ratio performance compared to previously reported adaptive RAB algorithms.