AWB-FCNN Algorithm for Mainlobe Interference Suppression

As one of the promising technologies of wideband beamforming, the anti-mainlobe interference wideband beamforming (AWB) algorithm can effectively suppress mainlobe distortion and sidelobe level rise, thereby improving the output signal to interference plus noise ratio (SINR) performance. Therefore, an AWB algorithm is proposed via a feature fusion convolutional neural network (FCNN) in this paper, named as AWB-FCNN algorithm. It can improve the beamforming performance and ensure the computational efficiency. For this algorithm, an AWB algorithm firstly is used to generate the network training label. Then, an FCNN model is constructed to predict beamforming weight vectors, which consists of a feature extraction module, a feature fusion module, and a weight vector prediction module. Specially, an atrous convolution layer is introduced into the feature extraction module to extract dense features, which be achieved by enlarging the receptive field without increasing the parameters of the network. Besides, the feature fusion module is used to reduce the irrelevant features such as mainlobe interference by fusing features at different scales. Finally, the well-trained FCNN model can rapidly and precisely output beamforming weight vectors. Simulation results show that the proposed algorithm has excellent interference suppression ability and high computational efficiency.