A neural network approach for regional vertical total electron content modelling |
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Authors: | R F Leandro M C Santos |
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Institution: | (1) Department of Geodesy and Geomatics Engineering, University of New Brunswick, P.O. Box 4400, Fredericton, N.B., E3B 5A3, Canada |
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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. |
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Keywords: | total electron content ionosphere regional ionospheric model neural network |
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