Broadband Beamforming Weight Generation Network Based on Convolutional Neural Network

Adaptive broadband digital beamforming (ABDBF) is an essential topic in the realm of array antenna for radar systems because the array antenna with ABDBF could obtain a wide swath and high azimuth resolution. Performance degradation at low snapshots is a serious problem for ABDBF. In this letter, the broadband beamforming weight generation network (BWGN) is proposed to quickly generate weights for ABDBF under low signal snapshot scenarios. The BWGN leverages the complex convolutional neural network (CNN) to represent the mapping between input signals and output weights, which avoids the operation of the covariance matrix and its inversion, thus speeding up the generation of weights. Compared with the existing neural network-based broadband beamforming method, wideband beamforming prediction network (WBPNet), training BWGN saves 71.45% of the time. Furthermore, progressive learning (PL) is introduced to the training process of BWGN, namely PL-BWGN, which further reduces the training time of BWGN to 0.7529 h (h: hours). Simulation results demonstrate the performance superiority of the proposed method compared with existing beamformers under low snapshot scenarios.