Classification of Underwater Objects Via Impulse Excitation

Abstract

A technique for classifying objects based on modeling the transient characteristics of their impulse response is developed and tested. A set of targets identical in geometry and differing in shell and filler material were constructed. The targets were manually struck exciting an impulse response which was sampled and recorded. The impulse response of each target was decomposed via windowed short-time Fourier transform into a set of feature vectors. The feature vectors were quantized via the LBG VQ algorithm, and the sets of quantized vectors were used to estimate the parameters of a discrete-output hidden Markov model (HMM) for each class of object. A blind test set was evaluated against the trained HMMs and the results are presented along with a discussion of the generalization ability of the individual classifiers.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA496830

Entities

People

  • Charles Bernstein
  • J. T. Cobb
  • Rodolfo Arrieta

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acoustic Signals
  • Algorithms
  • Classification
  • Construction Materials
  • Excitation
  • Frequency
  • Hidden Markov Models
  • Machine Learning
  • Markov Models
  • Materials
  • Models
  • Neural Networks
  • Probability
  • Probability Distributions
  • Random Variables
  • Surface Warfare
  • Underwater Objects

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Speech Processing/Speech Recognition.
  • Structural Dynamics.