Backpropagation Neural Network for Noise Cancellation Applied to the NUWES Test Ranges

Abstract

This thesis investigates the application of backpropagation neural networks as an alternative to adaptive filtering at the NUWES test ranges. To facilitate the investigation, a model of the test range is developed. This model accounts for acoustic transmission losses, the effects of doppler shift, multipath, and finite propagation times delay. After describing the model, the backpropagation neural network algorithm and feature selection for the network are explained. Then, two schemes based on the network's output, signal waveform recovery, and binary code recovery are applied to the model. Simulation results of the signal waveform recovery and direct code recovery schemes are presented for several scenarios.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA246997

Entities

People

  • Charles H. Wellington Jr

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Algorithms
  • Carrier Frequencies
  • Doppler Effect
  • Electrical Engineering
  • Feature Selection
  • Filtration
  • Frequency
  • Frequency Response
  • Losses
  • Neural Networks
  • Power Spectra
  • Signal Processing
  • Simulations
  • Transmission Loss
  • Transmitters
  • Waveforms
  • Waves

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks