Deep transfer learning for underwater direction of arrival using one vector sensora)

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.

Automated summary

A deep transfer learning method is proposed for the direction of arrival estimation using a single-vector sensor, involving CNN training with synthetic source data and adaptation to target domain with at-sea data. By feeding the CNN with acoustic pressure and particle velocity cross-spectra, reliable DOA estimates of a moving surface ship are achieved, outperforming conventional CNNs, especially in the presence of interfering sources.