ROTHR Mode-Linking Using Neural Networks

Abstract

A novel concept using neural network technology was employed to find the correct linkage of ionospheic modes in a multi-mode multi-target environment given a model of the ionosphere. The technology employed consists of a self-organizing feature map (SOFM) to provide separation of multiple targets containing multiple modes followed by an Adaptive Resonance Theory Map (ARTMAP) to perform spatial pattern recognition of ionospheric mode patterns and to link them to a single ground range. The ARTMAP recognition system is trained on the Coordinate Registration (CR) tables produced by ROTHR. The current CR tables are updated every 12 minutes, thus providing a real-time assessment of the ionosphere. The exactness of the mode-linking procedure consequently depends on the accuracy of the Coordinate Registration tables.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2002
Accession Number
ADA402459

Entities

People

  • R. L. Friedman
  • W. P. Gnadt

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Detection
  • Environment
  • Frequency
  • Geometry
  • Ionosphere
  • Ionospheric Models
  • Machine Learning
  • Military Research
  • Multiple Targets
  • Neural Networks
  • Over The Horizon Radar
  • Pattern Recognition
  • Ray Tracing
  • Recognition

Readers

  • Geodesy
  • Library and Information Science/ Studies, Southeast Asia Studies, Bibliography of Vietnam and Lao Studies.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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