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An overview of array invariant for source-range estimation in shallow water.

H C Song1, Gihoon Byun1

  • 1Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.

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|April 24, 2022
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Summary

The array invariant (AI) method offers a robust approach to localizing underwater acoustic sources by analyzing signal dispersion, outperforming traditional matched-field processing (MFP) in complex environments. This dispersion-based technique is effective even with environmental mismatches.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Oceanography

Background:

  • Traditional matched-field processing (MFP) for underwater acoustic source localization is sensitive to environmental variations.
  • Robust methods are needed to overcome the limitations of model-based processing in realistic ocean environments.

Purpose of the Study:

  • To provide an overview of the array invariant (AI) method for underwater acoustic source localization.
  • To highlight the physics, advantages, and recent developments of AI compared to MFP.

Main Methods:

  • The array invariant (AI) method exploits the dispersion characteristics of broadband signals in acoustic waveguides, summarized by the waveguide invariant (β).
  • AI utilizes conventional plane wave beamforming and coherent multipath arrivals for source-range estimation.
  • The method has been extended to range-dependent environments using iterative approaches and adaptive techniques.

Main Results:

  • AI demonstrates remarkable performance and robustness, validated with experimental data in shallow-water environments.
  • The method is applicable to realistic shallow-water conditions and has been successfully extended to range-dependent coastal environments.
  • Adaptive AI can handle the dependence of β on propagation angle, including steep-angle arrivals.

Conclusions:

  • The array invariant (AI) method provides a robust and effective alternative to traditional MFP for underwater acoustic source localization.
  • AI's ability to leverage signal dispersion makes it suitable for complex and dynamic oceanographic conditions.
  • Ongoing developments in AI, such as adaptive processing, enhance its applicability to a wider range of scenarios.