Using artificial neural networks for Meteor-Burst communications trail prediction

Stuart Melville, Geoffrey Sutcliffe, David Fraser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The use of meteor ionisation trails as ‘cheap satellites’ to reflect radio waves between two points on the earth’s surface is an established technique, called Meteor Burst Communications (MBC). For MBC systems to take advantage of the different amplitude and duration patterns of different trail types it is necessary to predict these patterns from features of initial signals reflected from the trails. The work described in this paper attempts to predict trail amplitude, duration, and trail type using neural networks. Results include a picture of what features of the beginning of the trail are most and least important for recognising various characteristics of the rest of the trail, some significant results as regards trail type prediction, and high correlations between actual and predicted peak amplitudes of trails. The latter is an important result.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages423-434
Number of pages12
Volume1114
ISBN (Print)3540615326, 9783540615323
DOIs
StatePublished - 1996
Externally publishedYes
Event4th Pacific Rim International Conference on Artificial Intelligence, PRICAI 1996 - Cairns, Australia
Duration: Aug 26 1996Aug 30 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1114
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th Pacific Rim International Conference on Artificial Intelligence, PRICAI 1996
CountryAustralia
CityCairns
Period8/26/968/30/96

Keywords

  • Applications to telecommunications
  • Neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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