Ionospheric Modeling and Forecasting Services of GNSS
Yellamma Pachipala1, Amarendra K2, Puvvada Nagesh3, K Sri Rama Vamsi4
1Dr Yellamma Pachipala*, Associate Professor, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, (A.P), India.
2Dr Amarendra K, Professor, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, (A.P), India.
3Puvvada Nagesh, Assistant Professor, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, (A.P), India.
4K Sri Rama Vamsi, Student, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, (A.P), India.
Manuscript received on November 27, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 5185-5189 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4245129219/2019©BEIESP | DOI: 10.35940/ijeat.B4245.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The study of ionospheric variability is vital for refining and predicts services of GNSS (Global Navigation Satellite System) applications. The GNSS service provides an explicitly obtainable resource GNSS information, goods and services in support of the earthly reference structure. GNSS is provides an Earth observation, navigation, positioning, timing and other applications that an assistance Society and Science. To investigate the variations in the daily averaged TEC (Total Electron Content) by taking several model components such as solar activity component, geomagnetic activity and periodic components at different latitudes ranging from 10˚N to 26˚N for the year 2018. The presented results would be useful to download, processing and analysis the IGS (International GNSS Service) data. MSSA model can reproduce quite well the observed values of GPS-TEC by utilizing only the first 4 singular modes and constitutes 99% of the total variance. The data is transformed into the singular values which are used for forecasting a noiseless time series. The proposed system results show that higher accuracy is achieved by MSSA (Multivariate Singular Spectrum Analysis) based model. It can also be noted that the training of MSSA is much faster and achieves higher learning accuracy, lowest training time. MSSA is effective even the space weather conditions are active during different solar phase periods.
Keywords: GNSS, MSSA, International GNSS Service, Global Positioning System, Total electron content.