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Perception-based automatic classification of impulsive-source active sonar echoes.

Victor W Young1, Paul C Hines

  • 1Defence R&D Canada-Atlantic, Box 1012, Dartmouth, Nova Scotia B2Y 3Z7, Canada. victor.young@drdc-rddc.gc.ca

The Journal of the Acoustical Society of America
|October 12, 2007
PubMed
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This study introduces an automatic classifier for active sonar echoes, using perceptual signal features from musical acoustics to distinguish between true targets and environmental clutter. The classifier achieves high accuracy, significantly improving sonar performance by reducing false alarms.

Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Impulsive-source active sonar systems frequently generate false alarms due to environmental clutter.
  • Distinguishing true target echoes from clutter echoes is crucial for enhancing sonar performance.

Purpose of the Study:

  • To implement an automatic classifier for active sonar echoes.
  • To utilize perceptual signal features, inspired by musical acoustics and timbre, for echo discrimination.

Main Methods:

  • The study employed perceptual signal features, including loudness function centroid/peak and subband attack/decay times.
  • These features were used to train and test an automatic classifier.
  • A receiver operating characteristic (ROC) curve analysis was performed.

Related Experiment Videos

Main Results:

  • The automatic classifier demonstrated a capability to discriminate between target and clutter echoes.
  • An equal error rate of approximately 10% was achieved.
  • The area under the ROC curve reached 0.975, indicating high classification performance.

Conclusions:

  • Perceptual signal features derived from musical acoustics are effective for active sonar echo classification.
  • The developed automatic classifier significantly improves the discrimination between targets and clutter.
  • This approach offers a promising method for reducing false alarms in sonar systems.