Passive Sonar Target Recognition Using a Back-Propagating Neural Network

Abstract

The prompt and accurate processing of sonar data is essential in undersea warfare. The ability to quickly detect and classify sonar targets is crucial to the performance and survivability of all navy surface ships and submarines. With the advent of neural network technology, new opportunities have arisen which could greatly enhance current sonar target recognition capabilities. The main objective of this research is to demonstrate the practical usage of neural networks in recognizing the acoustic signatures of passive sonar targets using simulated-at-sea conditions. We will review the theory behind neural networks, the problems associated with recognizing acoustic signals in an underwater environment, and we will make a detailed case study of a neural network's performance using test data generated from simulated sonar targets.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1991
Accession Number
ADA247029

Entities

People

  • David F. Moore

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Acoustic Signals
  • Ambient Noise
  • Cavitation Noise
  • Computer Programs
  • Computers
  • Detection
  • Digital Computers
  • Engineering
  • Frequency
  • Information Systems
  • Network Architecture
  • Neural Networks
  • Neurons
  • Recognition
  • Signal Processing
  • Target Recognition
  • Transmission Loss

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks