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

Low-Complexity Robust Adaptive Beamforming Algorithms Based on Shrinkage for Mismatch Estimation