WS-ICNN algorithm for robust adaptive beamforming

This paper presents a wideband signal (WS) beamforming method based on Inception convolutional neural network (ICNN), named as WS-ICNN algorithm. Firstly, an Inception module is constructed via some convolutional layers with feature maps of different sizes and a pooling layer. It can not only extract different scale information of covariance matrix, but also excavate the spatial correlation information about received wideband signals, so that the inception module can help neural network improve the beamforming output performance. On this basis, an ICNN model is established, which is suitable for wideband beamforming. Then, in order to obtain a good training label for the proposed ICNN model, a taper matrix and a second-order cone programming problem are introduced to calculate a wideband beamforming weight vector label. Based on this label, the training process of the proposed ICNN model is accomplished. Finally, the well-trained ICNN model accepts the input of the covariance matrix, and output the beamforming weight vector. Simulation results demonstrate the performance of the proposed algorithm in the cases of direction-of-arrival estimation error and sensor position error.