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Compressive sensing method to leverage prior information for submerged target echoes.

Tongjing Sun1, Philippe Blondel2, Bing Jia3

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This study introduces a compressive sensing method using prior information (CSPI) to enhance underwater target echo signals. CSPI significantly improves signal-to-noise ratio (SNR) and reduces data volume, even for noisy, low-visibility echoes.

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Marine technology

Background:

  • Marine environment noise significantly degrades underwater target echo signals.
  • Target echoes exhibit block-sparse features due to constituent highlights.
  • Improving signal-to-noise ratio (SNR) and reducing data volume are critical.

Purpose of the Study:

  • To develop a compressive sensing method leveraging prior information (CSPI).
  • To enhance the reconstruction of underwater target echoes.
  • To improve SNR and data efficiency in acoustic signal processing.

Main Methods:

  • Utilized a compressive sensing approach incorporating knowledge of incident waves and block-sparse echo features.
  • Developed a specialized dictionary structure for signal reconstruction.
  • Applied CSPI to simulated and field measurements of a submarine model.

Main Results:

  • CSPI successfully reconstructed near-invisible signals (0 dB SNR).
  • Achieved SNR improvements of up to 13 dB for simulated data.
  • Field measurements showed preserved SNR with 13% data and increased SNR with 30% data.

Conclusions:

  • CSPI effectively leverages prior information for robust underwater acoustic signal processing.
  • The method offers significant improvements in SNR and data compression for target echo analysis.
  • CSPI demonstrates practical utility in challenging marine acoustic environments.