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Summary
This summary is machine-generated.

This study introduces a deep neural network framework for pinpointing multiple underwater sound sources using horizontal arrays. The method effectively determines both direction and range, even with complex acoustic signal interactions.

Keywords:
deep neural networkmultiple source localizationshallow water environmentsubarray beamforming

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Deep neural networks (DNNs) excel at single underwater sound source localization.
  • Multiple source localization in shallow water is complex due to signal interactions.
  • Existing methods struggle with the challenges of multi-source acoustic environments.

Purpose of the Study:

  • To propose a novel deep neural network framework for multiple underwater source localization.
  • To accurately determine both the directions and ranges of multiple acoustic sources.
  • To develop a universal model applicable to arbitrary numbers of sources in complex scenarios.

Main Methods:

  • A two-stage deep neural network approach is employed for successive direction and range determination.
  • A feed-forward neural network is utilized for initial direction finding.
  • A long short-term memory recurrent neural network, combined with subarray beamforming, is used for source ranging, preserving crucial acoustic field correlations.

Main Results:

  • The proposed framework successfully localizes multiple underwater sound sources.
  • Subarray beamforming enhances signal extraction in the ranging stage, improving accuracy.
  • A single model trained on single-source data is effectively applied to multi-source scenarios.

Conclusions:

  • The developed two-stage DNN framework provides an effective solution for multiple underwater source localization.
  • The integration of subarray beamforming significantly enhances the performance of source ranging.
  • This approach offers a robust and versatile method for complex acoustic environments, validated by simulations and experiments.