A satellite-based method for modeling ionospheric slant TEC from GNSS observations: algorithm and validation

Abstract

Total Electron Content (TEC) modeling is critical for Global Navigation Satellite System (GNSS) users to mitigate ionospheric delay errors. The mapping function is usually used for Vertical TEC ionospheric correction models for slant and vertical TEC conversion. But the mapping function cannot characterize TEC variation in different azimuths between the user and satellites. The ionospheric modeling error resulting from the mapping function tends to be bigger in middle and low latitudes. Therefore, a new algorithm for ionospheric Slant TEC (STEC) modeling with Satellite-based Ionospheric Model (SIM) is proposed in this contribution. Validation tests are carried out with GNSS observation data from the Crustal Movement Observation Network of China during different solar activities and in different seasons. The performance of SIM is compared with that of several commonly-used Global Ionospheric Map (GIM) and Regional Ionospheric Map (RIM) products. The results show that the STEC bias and STD of SIM are within 1.0 TECU and about 2.0 TECU, respectively, and SIM can correct over 90% STEC RMS errors, outperforming the GIM and RIM products. Consequently, the SIM algorithm can be a new option for high-accuracy ionospheric delay correction in regional and local GNSS networks.

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

“…A review of the techniques for modeling the total electron content of the ionosphere can be found in Bust & Mitchell (2008). New developments are still ongoing (see for example Bidaine & Warnant (2010); Ansari et al (2017); Li et al (2020b); Yasyukevich et al (2015Yasyukevich et al ( , 2020Yasyukevich et al ( , 2022; Li et al (2021); Pudlovskiy (2021)); notably machine learning techniques have developed in this field like many others (Orus Perez, 2019;Mallika I et al, 2020;Ferreira et al, 2017). Typical examples of tomography reconstruction algorithm of the ionosphere in the literature, such as Seemala et al (2014), model the TEC over wide timescales (more than an hour, possibly more than a half day) and wide geographical regions (with cells hundreds of kilometers wide).…”

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

“…Then, ionospheric correction is attained from the local estimated ionospheric delay [13]. During the ionospheric delay, the spatial correlation is high, where a regional map containing SBAS map exposure which is enlarged through spatial prediction technique [14]. There are two analyses on this map, temporal and prediction, which consume the past and interior observations [15].…”

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

“…TEC fluctuates depending on the time of the day, season and year [10]- [12]. GNSS signals enable the monitoring of ionospheric behavior using either ground or space based GNSS receivers [13], [14]. Deep learning techniques characterize ionospheric states using prior ionospheric data under varied space weather situations [15]- [17].…”