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Underwater acoustic target recognition using attention-based deep neural network.

Xu Xiao¹, Wenbo Wang¹, Qunyan Ren¹

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

JASA Express Letters

|September 26, 2022

Summary

This summary is machine-generated.

This study introduces an attention-based neural network (ABNN) for accurate underwater ship recognition. The ABNN effectively focuses on target features and suppresses interference in complex marine environments.

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Area of Science:

Marine acoustics
Artificial intelligence
Signal processing

Background:

Underwater acoustic target recognition is challenging due to complex environments and multi-target interference.
Deep learning offers high accuracy but lacks interpretability in target recognition tasks.

Purpose of the Study:

To propose an attention-based neural network (ABNN) for improved underwater acoustic target recognition.
To enhance the interpretability of deep learning models in complex marine acoustic scenarios.

Main Methods:

Development of an attention-based neural network (ABNN) architecture.
Application of the ABNN to pressure spectrogram data with multi-source interference.
Utilizing an attention module for inspecting neural network operations.

Main Results:

The ABNN demonstrated a gradual focus on the frequency-domain features of the target ship.
Environmental noises and interference from other marine vessels were effectively suppressed.
High accuracy was achieved in both target detection and recognition.

Conclusions:

The proposed ABNN significantly improves underwater acoustic target recognition performance.
The attention mechanism enhances model interpretability by visualizing feature focus.
This approach is effective in challenging marine environments with multiple acoustic sources.