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Ionospheric studies for the implementation of GAGAN

TLDR
In this article, a near real-time grid-based ionospheric delay model for correcting propagation delay at 1575.42 MHz and 1227.6 MHz was proposed to meet the requirement of correction with 0.5 m maximum residue over Indian region.
Abstract
Satellite Based Augmentation System (SBAS), being developed by Indian Space Research Organization (ISRO) in collaboration with Airports Authority of India (AAI) is known as “GPS Aided GEO Augmented Navigation” (GAGAN). It is expected to offer better accuracy and integrity of navigation service than with GPS alone by providing correction terms to the GPS signals. This is achieved by modelling a Near Real Time Grid Based Ionospheric Delay Model for correcting propagation delay at 1575.42 MHz (L1) using measurements at 1575.42 and 1227.6 MHz (L2). Existing algorithms are replaced by Kriging based model to meet the requirement of correction with 0.5 m maximum residue over Indian region. Details of the data collection and pre-processing, including estimation of the Total Electron Content (TEC), which is a measure of ionospheric delay, has been described. Kriging algorithm and some preliminary results of studies are also presented in this paper. This includes the spatial decorrelation of the stochastic random field over the deterministic variation of ionospheric TEC. Its variation with time and locations are investigated and a temporal dependence found to exist. Large scale ionospheric irregularities and depletions that cause severe amplitude and phase scintillations are also studied. Their impacts on GAGAN are also shown. Some major scientific studies required to be carried out over Indian region to improve the GAGAN performance is discussed.

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Citations
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Two-Shell Ionospheric Model for Indian Region: A Novel Approach

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Effects of prolonged southward interplanetary magnetic field on low‐latitude ionospheric electron density

TL;DR: The role of electrodynamical/neutral-dynamical and compositional disturbances are discussed in view of these observations to understand low-latitude ionospheric response when geomagnetic disturbance persists for longer duration.
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Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

TL;DR: This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India using in-situ Learning Algorithm and finds that the mean and root mean square values of prediction errors remain small enough for all practical applications.
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Satellite‐based augmentation systems: A novel and cost‐effective tool for ionospheric and space weather studies

TL;DR: This paper proposes and demonstrates SBAS applicability to ionospheric and space weather research in a novel and cost‐effective way and vindicate the potential of SBAS over extended areas.
References
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Journal ArticleDOI

Robust detection of ionospheric irregularities

TL;DR: The approach outlined in this paper conservatively bounds the ionospheric errors even for the worst observed ionosphere conditions to date, using data sets taken from the operational receivers in the WAAS reference station network.

An Ionosphere Estimation Algorithm for WAAS Based on Kriging

Juan Blanch
TL;DR: It is shown that a carefully designed estimation algorithm based on kriging could provide confidence bounds on the ionospheric delay corrections allowing WAAS to meet the GNSS Landing System requirements.

An Assessment of the Current WAAS Ionospheric Correction Algorithm in the South American Region

TL;DR: In this paper, the authors used data from the South American region to perform a preliminary quantitative assessment of the performance of WAAS correction algorithms in this region, and found that the dominant error source for the WAAS planar fit algorithm is the inherent spatial variation of the equatorial ionosphere with ionospheric ��slant range delay residuals as high as 15 meters and root-consuming square residuals for the quiet day of 1.9 meters.
Journal ArticleDOI

An Assessment of the Current WAAS Ionospheric Correction Algorithm in the South American Region

TL;DR: In this paper, the authors found that the dominant error source for the WAAS planar fit algorithm is the inherent spatial variability of the equatorial ionosphere, with ionospheric slant range delay residuals as high as 15 m and root-mean-square (RMS) residuals for the quiet day of 1.9 m.
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