Ionospheric scintillation forecasting model based on NN-PSO technique

Ionospheric scintillation forecasting model based on NN-PSO technique The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Astrophysics and Space Science Springer Journals

Ionospheric scintillation forecasting model based on NN-PSO technique

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Physics; Astrophysics and Astroparticles; Astronomy, Observations and Techniques; Cosmology; Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) ; Astrobiology
ISSN
0004-640X
eISSN
1572-946X
D.O.I.
10.1007/s10509-017-3144-6
Publisher site
See Article on Publisher Site

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