Analysis of Acoustic Depth Sounder Signals with Artificial Neural Networks

Abstract

Research was conducted on 3 problems involving the analysis of acoustic depth sounder returns that concern hydrographic surveyors are addressed using artificial neural networks. The first problem is the detection of a suspended layer of material called fluff that lies above the top layer of the bottom and poses no obstruction to navigation, but appears to the conventional depth sounder as the hard bottom. The second problem involves classifying the top layer of material as to hard silty sand, hard silty clay, or soft clay. The third problem involves classifying the density of the top layer of material of the bottom. Neural network models based on the back propagation learning method are designed and tested. Hardware is proposed for the implementation of these models. The accuracy of the models developed is compared with a conventional mathematical method.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1991
Accession Number
ADA236845

Entities

People

  • Barry W. Mccleave

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Propagation
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Content Addressable Memory
  • Detection
  • Electrical Engineering
  • Materials
  • Mathematical Models
  • Navigation
  • Neural Networks
  • Parallel Computing
  • Signal Processing

Readers

  • Acoustical Oceanography.
  • Geotechnical Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks