Attention-Based Neural Network for Underwater Acoustic Target Detection and Direction-of-Arrival Estimation

Direction-of-arrival (DOA) estimation for underwater acoustic sources is usually affected by multisource interference and ambient noise. In this study, DOA estimation is achieved by using a conventional beamformer modified by attention mechanism (A-CBF) which explores the spatial spectrum for DOA estimation that can focuses more on the peak of the desired signal while suppressing other peaks caused by interference and noise. The coefficients in A-CBF are learned by a neural network trained by array-received signals. On the basis of the above concept, the neural network determines the presence of the target in the received signals. From data obtained during a 2020 sea trial, the A-CBF model was trained by using a small amount of experiment data. The processing results demonstrate its performance of DOA estimation and target detection through suppressing multisource interference and focusing on the beams of the target ship in the spatial spectrum.