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Recovering reverberation interference striations by a conditional generative adversarial network.

Bo Gao1, Jie Pang1, Xiaolei Li1

  • 1Department of Marine Technology, Ocean University of China, Qingdao 266100, China.

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This study uses a conditional generative adversarial network (CGAN) to clean reverberation interference striations (RISs) from shallow water sonar data. The method successfully recovers clear RISs, improving active sonar performance in noisy environments.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Shallow water sonar performance is limited by seafloor scattering, which distorts reverberation interference striations (RISs).
  • Random seafloor scattering poses a significant challenge for accurately interpreting active sonar data.

Purpose of the Study:

  • To develop a method for recovering clear RISs from distorted data in shallow water environments.
  • To extract the deterministic component of reverberation from stochastic scattering fields using advanced AI.

Main Methods:

  • A conditional generative adversarial network (CGAN) was employed to process and enhance distorted RIS data.
  • The CGAN model was trained and then applied to experimental data obtained from an explosive source.

Main Results:

  • The CGAN successfully recovered precise interference striations from distorted RIS data.
  • The method demonstrated robustness, performing effectively even at reverberation-to-noise ratios exceeding 2 dB.

Conclusions:

  • Conditional generative adversarial networks can effectively extract deterministic reverberation from stochastic scattering.
  • This technique offers a promising solution for improving active sonar performance in challenging shallow water conditions.