Performance of Neural Networks in Classifying Environmentally Distorted Transient Signals

Abstract

Neutral networks have been showing great promise in several areas, one of which is the classification of underwater acoustic transients. The classification of low-frequency underwater acoustic transient signals using a neural network based system is investigated. The received acoustic transients are simulated using a time-domain parabolic equation model. The neural network is trained on three source signals and tested by classifying the same signals at 25 different receiver locations in a noise-free, range-dependent (upslope) environment. Overall classification performance is above 90%.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA230739

Entities

People

  • E. J. Yoerger
  • P. K. Simpson
  • R. L. Field

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Broadband
  • Classification
  • Detection
  • Detectors
  • Distortion
  • Dynamics
  • Environment
  • Feature Extraction
  • Frequency
  • Machine Learning
  • Networks
  • Neural Networks
  • Oceans
  • Pattern Recognition
  • Power Spectra
  • Refraction
  • Time Domain

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks