Robust adaptive beamforming algorithm based on damped singular value decomposition regularization

Abstract

When adaptive beamformers are applied to actual problems, various model mismatches are present in the sample covariance matrix and signal steering vector, which severely deteriorates the performance of the beamformer. To address this issue, we develop a novel adaptive beamforming algorithm in which the weight vector is a linear combination of the presumed steering vector and basis vectors of the orthogonal supplement subspace of the steering vector. Then, we convert the common constrained optimization problem into an unconstrained one to establish the new beamformer. Moreover, we use damped singular value decomposition regularization, which employs the L-curve method to adaptively determine the regularized factor (loading level), for suppressing the effects of model mismatches. In addition, we perform simulations of the proposed algorithm and other existing beamforming algorithms by considering several commonly encountered mismatches (e.g., the direction-of-arrival mismatch, sensor gain, phase error and location perturbation, coherent local scattering, and mutual coupling) and demonstrate the superior performance of the new beamforming algorithm.