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Underwater Acoustic Matched Field Imaging Based on Compressed Sensing.

Huichen Yan1, Jia Xu2, Teng Long3

  • 1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. yanhc11@mails.tsinghua.edu.cn.

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|October 13, 2015
PubMed
Summary

This study introduces compressive sensing matched field processing (CS-MFP) to improve underwater target imaging and localization. The novel CS-MFP model enhances performance by addressing instability and nonuniqueness issues inherent in traditional methods.

Keywords:
coherence parametercoherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP)compressed sensing (CS)matched field processing (MFP)wave propagation

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Area of Science:

  • Acoustics
  • Signal Processing
  • Oceanography

Background:

  • Matched field processing (MFP) is crucial for underwater target imaging and localization.
  • Traditional MFP faces challenges due to underdetermined systems, leading to nonuniqueness and instability.
  • Exploiting target sparsity offers a potential solution to enhance MFP performance.

Purpose of the Study:

  • To propose a compressive sensing MFP (CS-MFP) model based on wave propagation theory.
  • To investigate the recovery performance and robustness of the CS-MFP model.
  • To develop an algorithm to address specific challenges in CS-MFP.

Main Methods:

  • A compressive sensing MFP (CS-MFP) model was developed using randomly deployed sensors.
  • Analysis of lower bounds for the coherence parameter of the CS dictionary.
  • Investigation of CS-MFP robustness against sensor displacement.
  • Development of a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm.

Main Results:

  • The proposed CS-MFP model demonstrates effectiveness in underwater target imaging and localization.
  • The study provides insights into the recovery performance and robustness of CS-MFP.
  • The CCOOMP algorithm effectively addresses high coherent dictionary issues in specific scenarios.

Conclusions:

  • CS-MFP offers a promising approach to overcome limitations of traditional MFP.
  • The developed model and algorithm enhance the reliability and accuracy of underwater acoustic imaging.
  • Numerical experiments validate the effectiveness of the proposed CS-MFP method.