Real Time TEC Prediction During Storm Periods using AR Based Kalman Filter
B. Arundhati1, V. GopiTilak2, S. KoteswaraRao3
1B. Arundhati, Professor, Department of Electrical & Electronics Engineering, Visakhapatnam (A.P), India.
2V. Gopi Tilak, Research Scholar, Department of ECE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram (A.P), India.
3S. Koteswara Rao, Professor, Department of ECE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram (A.P), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 261-265 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3492048619/19©BEIESP
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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: Ionosphere total electronic content (TEC) observations available from global navigation Satellite systems are random in nature and these can be described by a stochastic process. During geomagnetic storms, TEC values are further disturbed and the disturbance is also another stochastic process. In this paper, it is tried out to model the process using Kalman filter with autoregressive statistics. Realistic TEC data during quiet days and disturbed days with respect to the geomagnetic storm are modeled in terms of autoregressive coefficients and the original data is reconstructed to find out the accuracy of the process. In this paper, the model is applied for different storm periods (Geomagnetically Quiet to Greatly disturbed) in the span of 23rd and 24th solar cycles i.e., from 1996 to 2018 for a low latitude station Lucknow data and the observations are presented and analyzed graphically. The error values showed that the Kalman filter gives better prediction values
Keyword: Kalman Filter, Ionosphere, Total Electron Content.
Scope of the Article: Regression and Prediction