Classification of Underwater Signals Using a Back-Propagation Neural Network

Abstract

This thesis examines a number of underwater acoustic signals and the problem of classifying these signals using a back-propagation neural network. The neural network classifies the signals based upon features extracted from the original signals. The effect on classification by using an adaptive line enhancer for noise reduction is explored. Two feature extraction methods have been implemented; modeling by an autoregressive technique using the reduced-rank covariance method, and the discrete wavelet transformation. Both orthonormal and non-orthonormal transforms are considered in this study.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1997
Accession Number
ADA331774

Entities

People

  • Richard C. Bennett Jr

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Classification
  • Data Science
  • Electrical Engineering
  • Equations
  • Feature Extraction
  • Frequency Bands
  • Frequency Response
  • Information Science
  • Machine Learning
  • Neural Networks
  • Noise
  • Noise Reduction
  • Odontocetes
  • Signal Processing
  • Transfer Functions

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks