A Hybrid-Source Ranging Method in Shallow Water Using Modal Dispersion Based on Deep Learning

The relationship between modal elevation angle and the relative arrival time between modes, derived from exploiting modal dispersion, provides source information that is less susceptible to environmental influences. However, the standard method based on modal dispersion has limitations for application. To overcome this, we propose a hybrid method for passive source ranging of low-frequency underwater acoustic-pulse signals in a range-independent shallow-water waveguide. Our method leverages deep learning, utilizing the intermediate results from the standard method as inputs, and short-time conventional beamforming to transform signals received by a vertical line array into a beam-time-domain sound-intensity map. The source range is estimated using an attention-based regression model with a ResNet backbone that has been trained on the beam-time-domain sound-intensity map. Our experimental results demonstrate the superiority of the proposed method, with a mean relative-error reduction of 71%, mean root-squared error reduction of 2.25 km, and an accuracy of 85%, compared to matched-field processing.

Automated summary

The relationship between modal elevation angle and the relative arrival time between modes, derived from exploiting modal dispersion, provides less environmentally influenced source information. However, the standard method based on modal dispersion has limitations. To overcome this, we propose a hybrid method that combines deep learning with short-time conventional beamforming to estimate source range of low-frequency underwater acoustic-pulse signals. Experimental results show that our method outperforms matched-field processing, with a mean relative-error reduction of 71%, mean root-squared error reduction of 2.25 km, and an accuracy of 85%.