Classification of Underwater Signals Using Wavelet-Based Decompositions

Abstract

This thesis investigates the application of wavelet decompositions to classification applications. Two feature extraction tools are considered: Local Discriminant Bases scheme (LDB) and Power method. Several dimension reduction schemes including a newly proposed one called the Mean Separator neural network (MS NN) are discussed. Two types of classifiers are investigated and compared: Classification Trees (CT) and Back-propagation neural network (BP NN). Classification experiments conducted on synthetic and real-world underwater signals show that: (1) the Power feature extraction method is more robust to time synchronization issues than the LDB scheme is; (2) the MS NN scheme is a successful dimension reduction scheme that may be used with both LDB and Power feature extraction methods; and (3) the BP NN is a more powerful classifier than CT as it has fewer constraints than CT in partitioning the feature input space.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1998
Accession Number
ADA349588

Entities

People

  • Ozhan Duzenli

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computers
  • Dimensionality Reduction
  • Electrical Engineering
  • Feature Extraction
  • Feature Selection
  • Fourier Series
  • Frequency Bands
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Random Variables
  • Separators
  • Signal Processing
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space