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This study introduces a novel spatial spectrum reconstruction algorithm for underwater acoustic signal processing. It effectively suppresses unknown near-field interference without requiring prior position information, improving target detection.

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Area of Science:

  • Underwater acoustic signal processing
  • Array signal processing
  • Electromagnetics

Background:

  • Conventional spatial spectrum estimation struggles with strong near-field interference, degrading performance in underwater acoustics.
  • Existing interference suppression methods often require prior knowledge of interference parameters, limiting their applicability.

Purpose of the Study:

  • To propose a super-resolution spatial spectrum reconstruction algorithm for scenarios with unknown near-field interference.
  • To develop a method that mitigates interference without needing information on its position or magnitude.

Main Methods:

  • Leveraging rank constraint-based relaxation and alternating minimization for spatial spectrum reconstruction.
  • Developing a robust algorithm adaptable to unknown interference characteristics.

Main Results:

  • The proposed algorithm effectively reconstructs spatial spectra in the presence of unknown near-field interference.
  • Demonstrated superiority over traditional methods in resolution, denoising, and estimation accuracy.
  • Achieved comparable performance to methods using prior interference information, even with limited data or low SNR.

Conclusions:

  • The developed spatial spectrum reconstruction algorithm offers a robust solution for underwater acoustic signal processing challenges.
  • It provides enhanced performance in scenarios with unknown near-field interference, outperforming existing techniques.