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This study introduces a novel neural process method for acoustic field reconstruction in ocean waveguides. The approach enhances passive source localization accuracy and robustness, outperforming traditional methods.

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Area of Science:

  • Ocean acoustics
  • Signal processing
  • Machine learning

Background:

  • Accurate acoustic field reconstruction is crucial for underwater passive source localization.
  • Traditional methods face challenges with sparse data and computational efficiency.

Purpose of the Study:

  • To develop an advanced method for acoustic field reconstruction using complex-valued neural processes and modal depth functions (MDFs).
  • To improve the accuracy and robustness of passive source localization in ocean environments.

Main Methods:

  • Utilizing complex-valued neural networks to model acoustic field distributions with Gaussian processes.
  • Employing a meta-learning strategy for parameter optimization to prevent overfitting and accelerate reconstruction.
  • Integrating the reconstructed acoustic field with matched field processing (MFP) for localization.

Main Results:

  • The proposed method significantly improves localization performance on the SWellEx-96 waveguide.
  • The MDF-based kernel demonstrates superior performance over the Gaussian kernel in characterizing ocean waveguides.
  • The method effectively denoises, interpolates sparse data, and generates dense field data for virtual arrays.

Conclusions:

  • The complex-valued neural process method with MDFs offers enhanced acoustic field prediction and robust passive source localization.
  • This approach addresses limitations of traditional MFP and GP-based MFP, particularly in complex ocean environments.