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Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation

Dongri Xie1, Hamada Esmaiel2,3, Haixin Sun4

  • 1School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a new method for extracting features from ship-radiated noise (SRN) using enhanced variational mode decomposition and permutation entropy. The approach achieves 100% recognition accuracy for classifying SRN signals, outperforming existing techniques.

Keywords:
enhanced variational mode decompositionfeature extractionnormalized correlation coefficientpermutation entropyship-radiated noise

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Machine learning

Background:

  • Underwater acoustic channels are complex and variable, distorting ship-radiated noise (SRN) signals.
  • Existing entropy-based feature extraction methods are impractical for direct SRN signal application.
  • Conventional de-noising methods struggle with marine environmental noise.

Purpose of the Study:

  • To propose a novel feature extraction method for ship-radiated noise (SRN) signals.
  • To improve the accuracy and practicality of SRN signal classification.
  • To address limitations in de-noising and feature extraction for passive sonar applications.

Main Methods:

  • Enhanced Variational Mode Decomposition (EVMD) to decompose SRN signals into intrinsic mode functions (IMFs).
  • De-noising of noise-dominant IMFs before permutation entropy (PE) calculation.
  • Calculation of normalized correlation coefficient (norCC) and PE for signal-dominant IMFs.
  • Feature vector generation by summing weighted PE values.
  • Classification using a particle swarm optimization-based support vector machine (PSO-SVM).

Main Results:

  • The proposed method achieved a 100% recognition rate for SRN samples.
  • Demonstrated significantly higher accuracy compared to existing methods.
  • Successfully extracted robust features from distorted SRN signals.

Conclusions:

  • The novel feature extraction method based on EVMD, norCC, and PE is highly effective for SRN signals.
  • The PSO-SVM classifier provides accurate multi-class classification of SRN samples.
  • This approach offers a practical and superior solution for SRN feature extraction in challenging underwater environments.