A Deep Transfer Learning Based Source Ranging Method in Deep-Sea Environment

When employing the conventional beamforming (CBF) for the estimation of the direction of arrival of the Direct rays, one can observe a corresponding relationship between the arrival angle and the source distance, which can be used for range estimation. In the actual deep ocean environment, the arrival angle matched location method performs effectively in solving range estimation problems, although its performance is susceptible to the signal-to-noise ratio (SNR). To enhance the environmental adaptability and expand the application range of the source ranging method using the arrival structures in the beam do-main received by a vertical line array (VLA), we introduce a deep transfer learning (DTL) based source ranging method. Initially, a pre-trained model is established using simulation data generated under various SNRs through an ocean ambient noise model. Then high SNR experimental data is employed for DTL of the pre-trained model to fine tune the parameters. Finally, the experimental datasets are used to test the performance of the proposed method, and results suggest that the performance of the deep transferred model is much better than those of the traditional arrival angle matched location method and the model trained on noise-free data.