Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data

Zheng, Dunyong; Yao, Yibin; Nie, Wenfeng; Mengguang, Liao; Ji, Liang; Ao, Minsi

doi:10.1109/tgrs.2020.3029819

Cited by 14 publications

(5 citation statements)

References 48 publications

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“…(2019) and further validated by Zheng et al. (2020), we use VTEC data together with STEC to increase the spatial coverage of data. To improve the solution over the oceans, we have incorporated VTEC observations obtained by the Global Ionospheric Maps (GIMs) from CODE, which provides a smooth interpolation of the data gaps.…”

Section: Tomography Formulationmentioning

confidence: 94%

“…However, a major issue on the global tomography relates to the sparse GNSS receiver locations, leading to several data gaps. In this study, similar to Prol et al (2019) and further validated by Zheng et al (2020), we use VTEC data together with STEC to increase the spatial coverage of data. To improve the solution over the oceans, we have incorporated VTEC observations obtained by the Global Ionospheric Maps (GIMs) from CODE, which provides a smooth interpolation of the data gaps.…”

Section: Tomography Formulationmentioning

confidence: 99%

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Global‐Scale Ionospheric Tomography During the March 17, 2015 Geomagnetic Storm

et al. 2021

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Measuring the electron density in the ionosphere is an important step to improve our understanding of the solar-terrestrial environment impact on communication, surveillance, and navigation systems. Several methods can be applied to estimate the three-dimensional (3D) electron density in the ionosphere (Bust & Mitchell, 2008). In this context, the computerized ionospheric tomography (CIT) gained attention in the last three decades since it provides accurate observations of the ionospheric electron density over large areas (Austen et al., 1988;Norberg et al., 2018). The main products are 3D spatial fields of the electron density, which evolve with time in a series of snapshots. Over the last 20 years, several achievements have been obtained by CIT techniques. It has been demonstrated that these images can be used to describe the overall ionospheric plasma structure and its temporal evolution in the atmosphere. They have been successfully used to represent important ionospheric dynamics, such as traveling ionospheric disturbances (Bolmgren et al., 2020;Chen et al., 2016), geomagnetic storm signatures

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Section: Tomography Formulationmentioning

confidence: 94%

Section: Tomography Formulationmentioning

confidence: 99%

Global‐Scale Ionospheric Tomography During the March 17, 2015 Geomagnetic Storm

et al. 2021

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“…Hernández-Pajares et al (1999) first presented GNSS-based data-driven tomographic models. Since then, many scholars further improved the ionosphere tomographic models by different methods (Wen et al, 2015;Wen et al, 2007Wen et al, , 2008Zheng et al, 2017Zheng et al, , 2018Zheng et al, , 2020Zheng et al, , 2021. Olivares-Pulido et al (2019) presented a 4D tomographic ionospheric model to support PPP-RTK, achieving an accuracy better than 10 cm in the horizontal direction within about 10 min.…”

Section: Introductionmentioning

confidence: 99%

Investigating GNSS PPP–RTK with external ionospheric constraints

et al. 2022

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Real-Time Kinematic Precise Point Positioning (PPP–RTK) is inextricably linked to external ionospheric information. The PPP–RTK performances vary much with the accuracy of ionospheric information, which is derived from different network scales, given different prior variances, and obtained under different disturbed ionospheric conditions. This study investigates the relationships between the PPP–RTK performances, in terms of precision and convergence time, and the accuracy of external ionospheric information. The statistical results show that The Time to First Fix (TTFF) for the PPP–RTK constrained by Global Ionosphere Map (PPP–RTK-GIM) is about 8–10 min, improved by 20%–50% as compared with that for PPP Ambiguity Resolution (PPP-AR) whose TTFF is about 13–16 min. Additionally, the TTFF of PPP–RTK is 4.4 min, 5.2 min, and 6.8 min, respectively, when constrained by the external ionospheric information derived from different network scales, e.g. small-, medium-, and large-scale networks, respectively. To analyze the influences of the optimal prior variances of external ionospheric delay on the PPP–RTK results, the errors of 0.5 Total Electron Content Unit (TECU), 1 TECU, 3 TECU, and 5 TECU are added to the initial ionospheric delays, respectively. The corresponding convergence time of PPP–RTK is less than 1 min, about 3, 5, and 6 min, respectively. After adding the errors, the ionospheric information with a small variance leads to a long convergence time and that with a larger variance leads to the same convergence time as that of PPP-AR. Only when an optimal prior variance is determined for the ionospheric delay in PPP–RTK model, the convergence time for PPP–RTK can be shorten greatly. The impact of Travelling Ionospheric Disturbance (TID) on the PPP–RTK performances is further studied with simulation. It is found that the TIDs increase the errors of ionospheric corrections, thus affecting the convergence time, positioning accuracy, and reliability of PPP–RTK.

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“…Chen et al [10] and Seemala et al [11] used the prior information extracted from an empirical model to restrain the tomographic model. Zheng et al [12] introduced the constraint of vertical total electron content data into their tomographic model. He and Heki [13], and Wen et al [14] restrained their tomographic models by the constraints of the horizontal continuity or the similarity among adjacent voxels.…”

Section: Introductionmentioning

confidence: 99%

Ionosphere Tomographic Model Based on Neural Network with Balance Cost and Dynamic Correction Using Multi-Constraints

Zhu

Dai

et al. 2022

Atmosphere

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A Neural network (NN) is a promising tool for the tomographic inversion of the ionosphere. However, existing research has adopted an unbalanced cost function for training purposes and a preset image for constraint purposes, resulting in the output image being dominated by measurements. To address these problems, we proposed an NN-based tomographic model with a balance cost function and a dynamic correction process (BCDC) for ionosphere inversion. The cost function is composed of two balance terms corresponding to the measurements and the selected constraints, respectively. The produced image in the forward process of the NN is corrected dynamically by fitting each vertical profile with orthogonal basis functions (EOFs) and the Chapman function and then by smoothing the voxels of each layer with a moving window approach horizontally. The corrected image is then used to calculate the slant total electron content (STEC) parameter, which is further translated into the term of the cost for the vertical and horizontal constraints. Experiments were carried out to validate the BCDC method and compared with a recently developed tomographic method and the international reference ionosphere (IRI) model. Results show that the parameters derived from the BCDC model demonstrate good consistency with the observations. Comparing with the reference methods, the BCDC method performs better in the validations of vertical profiles, F2 layer peak density (NmF2), STEC parameter and vertical total electron content map. Further analysis also shows that a balance cost function is of benefit to achieve an image of better quality.

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Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data

Cited by 14 publications

References 48 publications

Global‐Scale Ionospheric Tomography During the March 17, 2015 Geomagnetic Storm

Global‐Scale Ionospheric Tomography During the March 17, 2015 Geomagnetic Storm

Investigating GNSS PPP–RTK with external ionospheric constraints

Ionosphere Tomographic Model Based on Neural Network with Balance Cost and Dynamic Correction Using Multi-Constraints

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