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Surface and underwater acoustic target recognition using only two hydrophones based on machine learning

  • 0College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
The Journal of the Acoustical Society of America +

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June 4, 2024

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June 4, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Surface and underwater acoustic target recognition is improved using two hydrophones and a Gradient Boosting Decision Tree. This method significantly enhances accuracy compared to single hydrophone systems, achieving up to 100% for surface targets.

Area Of Science

  • Ocean acoustics
  • Signal processing
  • Machine learning

Background

  • Distinguishing surface and underwater acoustic targets is challenging due to noise interference.
  • Previous single hydrophone methods for passive sonar target recognition suffered from limited accuracy.

Purpose Of The Study

  • To develop an accurate method for surface and underwater (S/U) acoustic targets recognition.
  • To investigate the effectiveness of using two hydrophones with a Gradient Boosting Decision Tree for improved target identification.

Main Methods

  • A Gradient Boosting Decision Tree model was employed for S/U acoustic targets recognition.
  • A large training dataset was generated using the KRAKEN acoustic model.
  • Vertical linear array feature extraction was used to assimilate heterogeneous simulation and experimental data.

Main Results

  • The proposed two-hydrophone system achieved 100% accuracy for surface targets and 0.9384 for underwater targets using SACLANT 1993 data.
  • Single hydrophone recognition accuracy was significantly lower, at 0.4715 for surface and 0.5620 for underwater targets.
  • The model successfully utilized channel information alongside source spectrum information for recognition.

Conclusions

  • The two-hydrophone approach significantly outperforms single hydrophone systems in S/U acoustic target recognition.
  • The integration of acoustic modeling, feature extraction, and machine learning provides a robust solution for passive sonar applications.
  • This method offers a promising advancement for accurate underwater acoustic target identification.