Phase Detection Using Neural Networks.

Abstract

A likelihood of detecting a reflected signal characterized by phase discontinuities and background noise is enhanced by utilizing neural networks to identify coherency intervals. The received signal is processed into a predetermined format such as a digital time series. Neural networks perform different tests over arbitrary testing intervals to determine the likelihood of a phase discontinuity occurring in any such interval. An integration time generator subsequently uses this information to define a series of contiguous coherency intervals over the duration of the received signal. These coherency intervals are then used for piece-wise processing of the received signal by parallel quadrature receivers. The outputs are combined and processed for detecting the presence of the reflected signal.

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Document Details

Document Type
Technical Report
Publication Date
Mar 10, 1997
Accession Number
ADD018592

Entities

People

  • Christopher Deangelis
  • Robert C. Higgins

Organizations

  • United States Department of the Navy

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Analog Signals
  • Background Noise
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Discontinuities
  • Frequency
  • Generators
  • Inventions
  • Learning
  • Neural Networks
  • Noise
  • Preprocessing
  • Probability
  • Signal Detection
  • Signal Processing
  • Waves

Readers

  • Approximation Theory.
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

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