Transient Sonar Signal Classification Using Hidden Markov Models and Neural Nets

Abstract

In ocean surveillance, a number of different types of transient signals are observed. These sonar signals are waveforms in one dimension (1-D). The hidden Markov model (HMM) is well suited to classification of 1-D signals such as speech. In HMM methodology, the signal is divided into a sequence of frames, and each frame is represented by a feature vector. This sequence of feature vectors is then modeled by one HMM. Thus, the HMM methodology is highly suitable for classifying the patterns that are made of concatenated sequences of micro patterns. The sonar transient signals often display an evolutionary pattern over the time scale. Following this intuition, the application of HMM's to sonar transient classification is proposed and discussed in this paper.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA281212

Entities

People

  • Amlan Kundu
  • Charles E. Persons
  • George C. Chen

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Acoustic Surveillance
  • Algorithms
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Hidden Markov Models
  • Image Processing
  • Markov Models
  • Neural Networks
  • Ocean Surveillance
  • Pattern Recognition
  • Power Spectra
  • Probability
  • Recognition
  • Signal Processing
  • Surveillance
  • Undersea Surveillance

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.