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Subspace array processing using spatial time-frequency distributions: applications for denoising structural echoes of

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This study introduces a novel subspace array processing method for denoising non-stationary underwater structural echoes. The method utilizes a space-time-frequency distribution (STFD) to effectively separate target echoes from noise and clutter.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Target detection and classification

Background:

  • Structural echoes from underwater elastic targets are crucial for detection and classification.
  • These echoes are often non-stationary and aspect-dependent, posing challenges for traditional array processing methods.
  • Existing methods require signal stationarity and multiple snapshots, which are not met by distributed aperture recordings.

Purpose of the Study:

  • To develop a subspace array processing method for denoising non-stationary signals.
  • To address the limitations of common methods when dealing with structural echoes from distributed apertures.
  • To enable effective denoising of underwater target echoes even with single-snapshot, non-stationary data.

Main Methods:

  • Introduced a subspace array processing method based on the space-time-frequency distribution (STFD).
  • Calculated STFD by computing Cohen's class time-frequency distributions between pairwise signal combinations along an arbitrary aperture array.
  • Interpreted STFD as a generalized array covariance matrix accounting for time-frequency coherence of non-stationary echoes.

Main Results:

  • The STFD effectively captures the coherence of non-stationary echoes across the time-frequency plane.
  • Identifying the signal subspace from the STFD's eigenstructure enables denoising.
  • Numerical and experimental results demonstrated successful denoising of structural echoes from a thin steel spherical shell using a synthetic aperture.

Conclusions:

  • The proposed STFD-based subspace array processing method is effective for denoising non-stationary underwater structural echoes.
  • This approach overcomes the limitations of traditional methods by handling single-snapshot, non-stationary data.
  • The method offers a robust solution for improving the detection and classification of underwater elastic targets.