Robust Adaptive Beamforming Using Support Vector Machines

Notice of Violation of IEEE Publication Principles

“Robust Adaptive Beamforming Using Support Vector Machines”

by Xiaoying Ren, Yingmin Wang, Tuo Guo, Qi Wang
in IEEE Access, Volume 8, July 2020
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper contains content copied from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“Robust Array Beamforming With Sidelobe Control Using Support Vector Machines”
by César C. Gaudes, Ignacio Santamaría, Javier Vía, Enrique Masgrau Gómez, Talesa Sesé Paules
in the IEEE Transactions on Signal Processing, Vol. 55, No. 2, February 2007


A robust adaptive beamformer based on the support vector machines is proposed to solve the rapid degradation of the performance of minimum variance distortionless response beamformer when the steering vector is mismatched. The proposed beamformer assumes that the angle error range in the steering vector can be estimated; the array only responds within this range, and the array response outside the angle error range is considered as the abnormal response. The proposed method is expressed in the form of support vector regression, and Vapnik’s
$\varepsilon $
-insensitive quadratic loss function which can improve the robustness of beamformer is used as a regular term to punish the abnormal response. The assumption that the array only responds within a certain angle range reflects the sparsity of support vector regression and improves the ability of the algorithm against steering vector mismatch because the energy conservation theorem holds that the response is large. The regular term ensures that a deep null exists in the direction of interference, the interference can be effectively suppressed. The iterative reweighted least square procedure can converge to the real solution of the SVM regression problem and is easy to calculate; thus, it is used to solve the algorithm proposed in this study. The effectiveness of the algorithm is verified through computer simulations.