Evaluation of the SVM-based adaptive beamformer in mismatch and no-mismatch scenarios

In this paper, we evaluate the performance of the support vector machine (SVM) based adaptive beamforming technique in mismatch and no-mismatch scenarios and compare its performance with other well-known adaptive beamformers. This algorithm generalizes the conventional linearly constrained minimum variance cost function by including a regularization term that penalizes differences between the actual and presumed array response. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of the SVM-based beamformer in comparison with existing adaptive beamforming algorithms in mismatch scenarios.