  In this paper, we propose low-complexity robust adaptive beamforming (RAB)
techniques that based on shrinkage methods. The only prior knowledge required
by the proposed algorithms are the angular sector in which the actual steering
vector is located and the antenna array geometry. We firstly present a
Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm to
estimate the desired signal steering vector mismatch, in which the
interference-plus-noise covariance (INC) matrix is estimated with Oracle
Approximating Shrinkage (OAS) method and the weights are computed with matrix
inversions. We then develop low-cost stochastic gradient (SG) recursions to
estimate the INC matrix and update the beamforming weights, resulting in the
proposed LOCSME-SG algorithm. Simulation results show that both LOCSME and
LOCSME-SG achieve very good output signal-to-interference-plus-noise ratio
(SINR) compared to previously reported adaptive RAB algorithms.
