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Studia Geophysica et Geodaetica

, Volume 51, Issue 2, pp 279–292 | Cite as

A neural network approach for regional vertical total electron content modelling

  • R. F. Leandro
  • M. C. Santos
Article

Abstract

A Neural Network model has been developed for estimating the total electron content (TEC) of the ionosphere. TEC is proportional to the delay suffered by electromagnetic signals crossing the ionosphere and is among the errors that impact GNSS (Global Navigation Satellite Systems) observations. Ionospheric delay is particularly a problem for single frequency receivers, which cannot eliminate the (first-order) ionospheric delay by combining observations at two frequencies. Single frequency users rely on applying corrections based on prediction models or on regional models formed based on actual data collected by a network of receivers. A regional model based on a neural network has been designed and tested using data sets collected by the Brazilian GPS Network (RMBC) covering periods of low and high solar activity. Analysis of the results indicates that the model is capable of recovering, on average, 85% of TEC values.

Key words

total electron content ionosphere regional ionospheric model neural network 

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Copyright information

© StudiaGeo s.r.o. 2007

Authors and Affiliations

  • R. F. Leandro
    • 1
  • M. C. Santos
    • 1
  1. 1.Department of Geodesy and Geomatics EngineeringUniversity of New BrunswickFredericton, N.B.Canada

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