Applying deep learning for underwater broadband-source detection using a spherical array

For improving passive detection of underwater broadband sources, a source-detection and direction-of-arrival-estimation method is developed herein based on a deep neural network (DNN) using a spherical array. Spherical Fourier transform is employed to convert the element pressure signals into spherical Fourier coefficients, which are used as inputs of the DNN. A Gaussian distribution with a spatial-spectrum-like form is adopted to design labels for the DNN. A physical model coupling underwater acoustic propagation and the spherical array is established to simulate array signals for DNN training. The introduction of white noise into the training data considerably enhances the detection capability of the DNN and effectively suppresses false estimation. The model's performance is evaluated based on its detection rate at a constant false alarm rate. Notably, the model does not rely on prior knowledge of the source's spectral features. Further, this study demonstrates that a DNN trained by one source can achieve multisource detection to a certain extent. The simulation and experimental processing results validate the broadband detection capability of the proposed method at varying signal-to-noise ratios.