Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

• Published in: Advances in space research Vol. 47; no. 1; pp. 115 - 123
• Main Authors: Acharya, Rajat, Roy, Bijoy, Sivaraman, M.R., Dasgupta, Ashish
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 04-01-2011; Elsevier
• Subjects: Adaptive Neural Network; Astronomy; Earth, ocean, space; Exact sciences and technology; External geophysics; In-situ Learning; Ionosphere; Prediction; Total Electron Content; India; Adaptive Neural Network; In-situ Learning; Ionosphere; Prediction; Total Electron Content; Recurrent neural nets; Space remote sensing; Satellite observation; Total electron content; Space research; Variance analysis; Equatorial anomaly; Satellite navigation; Neural networks; Critical frequency; Positions; Performance; Learning algorithm; Group delay
• DOI: 10.1016/j.asr.2010.08.016
• EISSN: 1879-1948
• ISSN: 0273-1177

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.