Validity of Different Global Ionospheric TEC Maps over Indian Region

a, S. K. Panda; Harikaa, B.; Vineetha, P.; Dabbakuti, J. R. K. Kumar; Akhila, S.; Srujanaa, G.

doi:10.1109/icac3n53548.2021.9725568

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Additional ionospheric models also include IRI, NeQuick2, IGS, BDGIM, etc. [23,39]. Choosing an adequate ionospheric model generally depends on many factors, including real-time availability and computational complexity.…”

Section: Overview Of the Ionospheric Delay Conceptmentioning

confidence: 99%

“…These maps are generated by analyzing dual-frequency measurements taken by their GNSS stations. According to [39], in 2021, there were eight such stations around the world, called analysis centers. In order to provide GIMs in a standardized format that is easy to exchange, IONosphere map EXchange format (IONEX) has been developed [7].…”

Section: Igs Model and Databasementioning

confidence: 99%

“…Final GIMs (11 days); • Rapid GIMs [39,47], which are lower-accuracy rapid solutions (<24 h); • A predicted solution (available 1-2 days in advance).…”

mentioning

confidence: 99%

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Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

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Section: Overview Of the Ionospheric Delay Conceptmentioning

confidence: 99%

Section: Igs Model and Databasementioning

confidence: 99%

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Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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Performance Analysis of Various Ionospheric Delay Corrections in Single-frequency GPS Positioning solution at a low latitude Indian location

Vankadara

Sarath

Viswaas

et al. 2022

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Storm-Time Relative Total Electron Content Modelling Using Machine Learning Techniques

2022

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Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30°W–50°E and latitudes 32.5°N–70°N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model’s performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model.

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Validity of Different Global Ionospheric TEC Maps over Indian Region

Cited by 3 publications

References 16 publications

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Performance Analysis of Various Ionospheric Delay Corrections in Single-frequency GPS Positioning solution at a low latitude Indian location

Storm-Time Relative Total Electron Content Modelling Using Machine Learning Techniques

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