Robust adaptive beamforming via residual convolutional neural network

Abstract

Aiming at the problem that the covariance matrix includes the desired signal and the signal steer vector mismatches will degrade the beamforming performance, an effective robust adaptive beamforming (RAB) approach is presented in this paper based on a residual convolutional neural network (RAB-RCNN). In the presented method, the RAB-RCNN model is designed by introducing a residual unit, which can extract the deeper features from the signal sample covariance matrix. Residual noise elimination and interferences power estimation are utilized to reconstruct the desired signal covariance matrix, and correct the mismatched steering vector (SV) by the eigenvalue decomposition of the reconstructed desired signal covariance matrix. The projection method is utilized to redesign the signal interference-plus-noise covariance matrix. Furthermore, the beamforming weight vector is calculated with the two parameters obtained before and used as the label of the RAB-RCNN model, The trained model can rapidly and precisely output the predicted beamforming weight vector without complex matrix operations, including the matrix inversion of the signal covariance matrix, so that the calculation time can be reduced for beamforming. Simulations demonstrate the robustness of the presented approach against SV mismatches due to the direction-of-arrival estimation error, sensor position error, and local scattering interference.