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An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine.

Xinwei Luo1, Yulin Feng1

  • 1Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|September 24, 2020
PubMed
Summary

This study introduces an improved underwater acoustic target recognition method using feature auto-encoding. This approach enhances recognition accuracy and adaptability compared to traditional feature extraction techniques for ship radiated noise.

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Area of Science:

  • Underwater acoustics
  • Machine learning
  • Signal processing

Background:

  • Underwater acoustic target recognition faces challenges in effective feature extraction and classification.
  • Traditional methods like LOFAR, MFCC, and GFCC may discard crucial information during data compression.

Purpose of the Study:

  • To develop an advanced underwater acoustic target recognition method.
  • To overcome limitations of traditional feature extraction by utilizing feature auto-encoding.

Main Methods:

  • Inputting normalized frequency spectrum data into a restricted Boltzmann machine for unsupervised feature auto-encoding.
  • Layer-by-layer extraction of deep data structures.
  • Classifying extracted features using a Backpropagation (BP) neural network.
Keywords:
ATRGFCCauto-encodingrestricted Boltzmann machineunderwater acoustic

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Main Results:

  • The proposed feature auto-encoding method demonstrated superior recognition accuracy.
  • The system exhibited better adaptability when tested on an actual ship radiated noise database.
  • Performance surpassed traditional hand-crafted feature extraction methods.

Conclusions:

  • Feature auto-encoding offers a more effective approach for underwater acoustic target recognition.
  • This method preserves essential information lost in conventional techniques.
  • The developed system shows significant potential for practical applications in underwater surveillance.