Variable pixel size ionospheric tomography

Zheng, Dunyong; Zheng, Hongwei; Wang, Yanjun; Nie, Wenfeng; Li, Chaokui; Ao, Minsi; Hu, Wusheng; Zhou, Wei

doi:10.1016/j.asr.2017.03.031

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Nevertheless, the difficulty of this model is the determination of the multiple sizes and the contribution of each singlescale model. Another model with variable voxel size presented by Zheng et al (2017) [31], who reconstructed the IED by a decomposition of the lower and upper ionosphere with different voxel sizes (see Figure 3 in [31]). According to the IED profile, the bottom part was parameterized to a smaller voxel size.…”

Section: Variable Voxel Sizementioning

confidence: 99%

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

Wan

2021

Remote Sensing

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Ionized by solar radiation, the ionosphere causes a phase rotation or time delay to trans-ionospheric radio waves. Reconstruction of ionospheric electron density profiles with global navigation satellite system (GNSS) observations has become an indispensable technique for various purposes ranging from space physics studies to radio applications. This paper conducts a comprehensive review on the development of voxel-based computerized ionospheric tomography (CIT) in the last 30 years. A brief introduction is given in chronological order starting from the first report of CIT with simulation to the newly proposed voxel-based algorithms for ionospheric event analysis. The statement of the tomographic geometry and voxel models are outlined with the ill-posed and ill-conditioned nature of CIT addressed. With the additional information from other instrumental observations or initial models supplemented to make the coefficient matrix less ill-conditioned, equation constructions are categorized into constraints, virtual data assimilation and multi-source observation fusion. Then, the paper classifies and assesses the voxel-based CIT algorithms of the algebraic method, statistical approach and artificial neural networks for equation solving or electron density estimation. The advantages and limitations of the algorithms are also pointed out. Moreover, the paper illustrates the representative height profiles and two-dimensional images of ionospheric electron densities from CIT. Ionospheric disturbances studied with CIT are presented. It also demonstrates how the CIT benefits ionospheric correction and ionospheric monitoring. Finally, some suggestions are provided for further research about voxel-based CIT.

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Section: Variable Voxel Sizementioning

confidence: 99%

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

Wan

2021

Remote Sensing

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“…An ionosonde probes the ionospheric structure by broadcasting a sweep of high frequencies, usually in the range of 0.1 to 30 MHz (Reinisch et al 2009). Due to its high accuracy, the ionosonde-derived IED profiles have been often employed as ground truth reference to validate alternative ionospheric techniques, e.g., tomography (Zheng et al 2017) and radio occultation (Habarulema and Carelse 2016). As shown in Fig.…”

Section: Validation Of Ied Profiles By Ionosondementioning

confidence: 99%

“…Due to the simple operation and easy solution, the voxelbased model is often exploited in ionospheric tomography studies (Wen et al 2012;Yang et al 2017;Zheng et al 2017;Jin and Li 2018). To circumvent the ill-posed problem, many algorithms have been developed over the years.…”

Section: Introductionmentioning

confidence: 99%

A new parameterized approach for ionospheric tomography

Chen

Dai

et al. 2019

GPS Solut

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GNSS ionospheric tomography technique is capable to reconstruct the high-quality 3D ionospheric electron density (IED) images with a relatively low cost. We present a new parameterized approach for refining the voxel-based ionospheric tomography modeling. This approach is different to most other voxel-based techniques as they assume a homogeneous IED distribution in each voxel that is unreasonable for the tomography modeling. In this method, IED of any point within a voxel is determined via vertically exponential interpolation and horizontally inverse distance weighted interpolation from the IED values at the eight corners of that voxel. The parameterized tomography is tested with real data collected over the period of June 1-30, 2015 from 45 GPS stations in south China. The superiority of the new parameterized method is verified by comparison with the traditional nonparametric method. The new parameterized method outperforms the traditional method by 12%, 10%, 5% and 2% for vertical resolutions of 25 km, 50 km, 75 km and 100 km, respectively, in the self-consistency validation by GPS data. Such improvements are 20%, 24%, 22% and 16%, respectively, when assessed by the Swarm in situ IEDs. In terms of the vertical layer discretization, configurations using the resolution of 25 km generally performs better than the other three vertical resolutions. Overall, the parameterized method using a vertical resolution of 25 km achieves the best performance from the comprehensive comparisons with ionospheric data derived by GPS, ionosonde and Swarm satellites.

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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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Variable pixel size ionospheric tomography

Cited by 11 publications

References 31 publications

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

A new parameterized approach for ionospheric tomography

Investigating GNSS PPP–RTK with external ionospheric constraints

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