A deep transfer learning (DTL) method is proposed for the direction of arrival (DOA) estimation using a single-vector sensor. The method involves training of a convolutional neural network (CNN) with synthetic data in source domain and then adapting the source domain to target domain with available at-sea data. The CNN is fed with the cross-spectrum of acoustical pressure and particle velocity during the training process to learn DOAs of a moving surface ship. For domain adaptation, first convolutional layers of the pre-trained CNN are copied to a target CNN, and the remaining layers of the target CNN are randomly initialized and trained on at-sea data. Numerical tests and real data results suggest that the DTL yields more reliable DOA estimates than a conventional CNN, especially with interfering sources.

A direction of arrival (DOA) estimation method based on a convolutional neural network (CNN) using an acoustic vector sensor is proposed to distinguish multiple surface ships in a selected frequency band. The cross-spectrum of the pressure and particle velocity are provided as inputs to the CNN, which is trained using data obtained by employing an acoustic propagation model under different environmental and source parameters. By learning the characteristics of acoustic propagation, the multisource distinguishing performance of the CNN is improved. The proposed method is experimentally validated using real data.

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