Classification of Underwater Acoustic Transients by Artificial Neural Networks

Abstract

Artificial neural networks have been trained using the backpropagation algorithm to classify a variety of model transient source signals. The networks were then tested on signals propagated to 25 different receiver sites by the time-domain parabolic equation model. Despite the interference effects from surface and bottom reflections, the classification accuracy is about 90% in the noise-free case, virtually identical to that of a nearest-neighbor classifier on the same problem. Classification in the presence of noise is considerably reduced; however, the redundancy provided by the multiple receivers in most cases allows the network to correctly classify all signals from sources on which it was trained. In addition, it shows a robustness in the presence of unknown signals not shown by the nearest-neighbor classifier.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA230081

Entities

People

  • Robert L. Field
  • Ronald L. Greene

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Availability
  • Classification
  • Equations
  • Machine Learning
  • Monitoring
  • Neural Networks
  • Redundancy
  • Reflection
  • Security
  • Time Domain

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks