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This study introduces an improved method for detecting submarines using their wake vortex characteristics. The technique enhances the accuracy of inverting submarine maneuvering states, even with weak or incomplete wake data.

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Area of Science:

  • Marine engineering
  • Ocean acoustics
  • Submarine technology

Background:

  • Advanced noise reduction in submarines challenges traditional acoustic detection.
  • Non-acoustic methods, like wake vortex analysis, are crucial for submarine detection.
  • Wake vortex characteristics degrade over time, complicating submarine state inversion.

Purpose of the Study:

  • To develop an improved method for inverting submarine maneuvering states using wake vortex information.
  • To address the challenges posed by weak and dissipating wake vortex features.
  • To enhance the accuracy of submarine detection through non-acoustic means.

Main Methods:

  • Established a random finite set for submarine wake vortex observation features.
  • Utilized a multiscale fusion module and attention mechanism to re-encode weak wake vortex image features.
  • Extracted salient features from wake vortex images for state retrieval.

Main Results:

  • Achieved an average inversion accuracy improvement of 1.27% for weak feature wake vortex images.
  • Demonstrated successful inversion of submarine maneuvering states with incomplete feature information.
  • Validated the algorithm's effectiveness in scenarios with weakened submarine wake vortex characteristics.

Conclusions:

  • The proposed method effectively inverts submarine maneuvering states from weak or incomplete wake vortex data.
  • This technique offers a foundation for submarine detection based on wake characteristics.
  • Improved accuracy in non-acoustic submarine detection is achieved, enhancing operational capabilities.