Autocovariance Ionospheric Prediction Model for GAGAN Applications
V.Nagireddy1, P.Karthik2, D.Venkata Ratnam3, P.S.Brahmanadam4, B.Sada Siva Rao5, K.Sarat Kumar6, M.Ravi Kumar7

1V.Nagireddy,Department of ECE, K L University, Guntur, India,
2P.Karthik, Department of ECE,K L University, Guntur, India,
3Dr.D.Venkata Ratnam, Department of ECE, K L University, Guntur,India,
4Dr. Potula, Department of ECE, K L University, Guntur,India
5Prof B. Sadasivarao, K L University, Guntur,India.
6K.Sarat Kumar, K L University, Guntur,India.
7M.Ravi Kumar, K L University, Guntur,India.

Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 24-27 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0658052312 /2012©BEIESP
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Abstract: Airports Authority of India (AAI) and Indian Space Research Organization (ISRO) are being jointly developing a satellite based augmentation system which is popularly known as GPS Aided Geo Augmented Navigation System (GAGAN) to cater civil aviation requirements in India. Forecasting of the ionospheric behaviour in advance can be used to set up early warnings of ionospheric threats for GAGAN system. In this paper, an ionospheric forecasting model is implemented on the basis of autocovariance method. The dual frequency GPS receiver’s data of Hyderabad (Geographic 17.410N,78.550 E) station located at the National Geophysical Research Institute (NGRI),Government of India is considered for the analysis. Time series of vertical Total Electron Content (TEC) for all visible satellites are calculated for quiet days and disturbed days. In this method, the first prediction point outside the data time interval in the future and in the past is computed and added at the beginning or at the end of data, respectively. Using this first prediction point, the next prediction point is computed consequently. Forecasting of ionospheric delay variations would be immensely useful for the protection of valuable communication satellites from space weather conditions

Keywords: Autocovariance, forecasting, gps, gagan, and tec.