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A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information

Dongri Xie1,2, Haixin Sun2, Jie Qi1

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

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

A new hybrid feature extraction method effectively denoises ship-radiated noise (SRN) signals using improved variational mode decomposition (IVMD) and permutation entropy (PE). This approach achieves high accuracy in underwater acoustic target recognition.

Keywords:
feature extractionimproved variational mode decompositionmaximal information coefficientpermutation entropyship-radiated noise

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Machine learning

Background:

  • Marine environmental noise and unstable underwater acoustic channels corrupt ship-radiated noise (SRN) signals.
  • Signal denoising is crucial for accurate underwater acoustic target recognition.

Purpose of the Study:

  • To develop a novel hybrid feature extraction scheme for denoising and recognizing SRN signals.
  • To improve the accuracy of underwater acoustic target identification.

Main Methods:

  • Improved variational mode decomposition (IVMD) to decompose SRN signals into intrinsic mode functions (IMFs).
  • Filtering of noise IMFs, followed by permutation entropy (PE) extraction.
  • Normalized maximal information coefficient (norMIC) to weigh PE values of retained IMFs.
  • Particle swarm optimization-based support vector machine (PSO-SVM) classifier for SRN sample identification.

Main Results:

  • The proposed hybrid method achieved a classification accuracy of 99.1667%.
  • Demonstrated significantly higher accuracy compared to existing methods.
  • The feature extraction scheme is effective for practical applications.

Conclusions:

  • The integrated IVMD, norMIC, and PE approach provides a robust solution for SRN signal feature extraction.
  • The method enhances the performance of underwater acoustic target recognition systems.
  • The proposed technique is suitable for real-world applications requiring accurate SRN signal identification.