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Underwater Target Perception in Local HOS Space.

Jue Gao1, Peiyi Zhu1

  • 1School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu 215500, Jiangsu, China.

Computational Intelligence and Neuroscience
|September 28, 2021
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Summary

This study introduces an underwater target perception architecture using acoustic imaging and high-order statistics (HOS) for region-of-interest (ROI) detection. The method effectively reconstructs targets, improving detection rates and reducing false alarms in underwater acoustics.

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Area of Science:

  • Marine technology
  • Acoustic imaging
  • Signal processing

Background:

  • Underwater target detection is challenging due to acoustic image limitations.
  • Existing methods struggle with unknown target sizes and numbers.

Purpose of the Study:

  • To propose a novel underwater target perception architecture.
  • To enhance the accuracy and reliability of underwater target detection.

Main Methods:

  • A three-stage process: acoustic imaging, local high-order statistics (HOS) space conversion, and region-of-interest (ROI) detection.
  • Utilizing the unique cube structure in local skewness space for ROI detection.
  • Employing focus processing and uncertain theoretical templates for target reconstruction.

Main Results:

  • Demonstrated improved performance in signal-to-noise ratio (SNR), detection rate, and false alarm rate.
  • Achieved effective target reconstruction even with unknown target size and number.
  • Experimental validation using multiple acoustic image sequences.

Conclusions:

  • The proposed architecture offers a robust solution for underwater target perception.
  • The method is generalizable to various underwater applications.
  • Significant improvements in detection accuracy and reconstruction fidelity were observed.