Maximum Likelihood Adaptive Neural Systems (MLANS) Application to High Frequency (HF) Propagation

Abstract

The feasibility of applying a model-based neural network technique to investigate the properties of ionospheric clutter observed in the operation of high frequency (HF) propagation systems was examined. Individual ionospheric clutter structures found in the amplitude-range-Doppler (ARD) spectra of over-the-horizon (OTH) radar data were successfully segmented and characterized. A multi-mode Gaussian clutter model was formulated using the Maximum Likelihood Adaptive Neural System (MLANS) to fit the observations. The results indicate that either a three or a four mode Gaussian model is sufficient for MLANS to segment and characterize the observed clutter. High Fidelity simulations of time slices of the raw data were achieved by combining time-varying Gaussian together with a time-varying uniform distribution to represent the noise floor. Each Gaussian mode (or model) is characterized by a time-varying set of three parameters: amplitude, Doppler spread, and Doppler shift.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1997
Accession Number
ADA339150

Entities

People

  • C. P. Plum
  • L. I. Perlovsky
  • T. C. Marzetta
  • V. H. Webb

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Amplitude
  • Command And Control
  • Computational Science
  • Department Of Defense
  • Detection
  • Doppler Effect
  • Frequency
  • Ground Clutter
  • Intensity
  • Multiple Hypothesis Tracking
  • Neural Networks
  • Probabilistic Models
  • Radar
  • Reliability
  • Signal Processing
  • Statistical Algorithms

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference