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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Deep transfer learning for source ranging: Deep-sea experiment results.

Wenbo Wang¹, Haiyan Ni¹, Lin Su¹

¹Key Laboratory of Underwater Acoustic Environment, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America

|November 2, 2019

Summary

This summary is machine-generated.

This study introduces deep transfer learning for underwater source ranging, improving accuracy by migrating knowledge from synthetic to real-world environments. The method enhances underwater acoustic positioning capabilities.

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Area of Science:

Oceanography
Acoustics
Machine Learning

Background:

Accurate underwater source ranging is crucial for marine applications.
Transferring knowledge from simulated to real environments presents challenges.

Purpose of the Study:

To propose a deep transfer learning method for enhancing underwater source ranging accuracy.
To validate the method's effectiveness in a deep-sea experimental setting.

Main Methods:

A deep neural network was trained on synthetic datasets.
The network was refined using collected experimental data for source ranging.
Performance was compared against traditional convolutional neural networks.

Main Results:

The proposed deep transfer learning method significantly improved ranging accuracy.
The approach demonstrated effective adaptation from synthetic to experimental domains.
Accuracy gains were validated through deep-sea experiments.

Conclusions:

Deep transfer learning offers a robust solution for improving underwater source ranging.
The method shows potential for broad application in related underwater acoustic tasks.