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Model-based deep learning for source localization struggles with sound speed profile mismatch. Integrating a range-dependent model and transfer learning improved performance, even with limited shallow-water experimental data.

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Area of Science:

  • Acoustic signal processing
  • Machine learning applications in oceanography

Background:

  • Model-based deep learning offers a solution for limited training data in source localization.
  • Sound speed profile (SSP) mismatch, especially in shallow waters with internal waves, degrades performance.

Purpose of the Study:

  • To integrate a range-dependent SSP model into deep learning for improved source localization.
  • To address performance degradation caused by SSP variations in underwater acoustics.

Main Methods:

  • Developed a deep learning approach incorporating a simple range-dependent SSP model.
  • Trained the network on simulated data generated using the range-dependent SSP model.
  • Applied transfer learning with limited experimental data for generalization.

Main Results:

  • The network demonstrated good performance on validation data.
  • The model generalized effectively to experimental test data after transfer learning.
  • Successfully mitigated performance degradation due to SSP mismatch in shallow-water environments.

Conclusions:

  • Integrating range-dependent SSP models enhances deep learning for source localization.
  • Transfer learning is effective for adapting models to real-world experimental data with limited samples.
  • The proposed method shows promise for robust underwater acoustic source localization.