Three‐dimensional ionospheric tomography reconstruction using the model function approach in Tikhonov regularization

Wang, Sicheng; Huang, Shucai; Xiang, Jie; Fang, Hanxian; Feng, Jian; Wang, Yu

doi:10.1002/2016ja023487

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…Thus generalize inverse has to be performed. Many methods have been employed to circumvent the problem of ill-posedness in GNSS tomographic equation, such as iterative algorithms (ART, Algebraic Reconstruction Technique) (Bender et al 2011;Yao et al 2014;Zheng et al 2015;Yang et al 2017;Jin and Li 2018), classical constrained solution (Hirahara 2000), Singular Value Decomposition (Bhuyan et al 2002;Wen et al 2008), Kalman filter approach (Nilsson and Gradinarsky 2006;Perler et al 2011), or regularization method (Wen et al 2012;Yao et al 2013b;Wang et al 2016). Among…”

Section: Fig 1 Schematic Representation Of Voxel Discretization For mentioning

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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Section: Fig 1 Schematic Representation Of Voxel Discretization For mentioning

confidence: 99%

A new parameterized approach for ionospheric tomography

Chen

Dai

et al. 2019

GPS Solut

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“…Yao et al (2014) classified them into non-iterative reconstruction methods and iterative reconstruction methods. The most classic non-iterative reconstruction methods are constrained least square method and Tikhonov regularization, which utilize constraints to overcome the deficiency of known condition (Chen et al 2016;Wang et al 2016). The constraints can come from smoothing conditions, empirical models, or additional measurements.…”

Section: Introductionmentioning

confidence: 99%

Virtual reference station-based computerized ionospheric tomography

Wan

et al. 2020

GPS Solut

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In computerized ionospheric tomography (CIT) with ground-based GNSS, the voxels without satellite-receiver ray traversing cannot be reconstructed directly. We present a CIT algorithm based on virtual reference stations (VRSs), called VRS–CIT, to decrease the number of unilluminated voxels and improve the precision of the estimated ionospheric electron density (IED). The VRSs are set at the nodes of grids with a 0.5° × 0.5° resolution in longitude and latitude. We generate the virtual observations with the observations from nearby six or three stations selected according to azimuths and distances. The generation utilizes multi-quadric surface fitting with six stations and triangular linear interpolation with three stations. With the virtual observations added, the IED distribution is reconstructed by the multiplicative algebraic reconstruction technique with the initial values obtained from IRI-2016. The performance of VRS–CIT is examined by using the data from 127 GNSS stations located in 24–46° N and 122–146° E to derive the IED every 30 min. The study focuses on April 29, 2014, with the adaptability of VRS–CIT analyzed by 12 days, evenly distributed around equinoxes and solstices of 2014. The accuracy of the virtual observation is about 1 TECU. Comparing to that derived from CIT with only real observations, the unsolvability of VRS–CIT declined by 4–12% for the whole region, and for the main area, the improvement can be up to 70%. Taking two IED profiles from radio occultation as reference measurements, the mean absolute error (MAE) of IED by VRS–CIT decreases by 6.88% and 8.43%, respectively. Comparing with slant total electron content (STEC) extracted from five additional GNSS stations, the MAE and the root mean square error of the estimated STEC can be reduced up to 17.24% and 33.81%, respectively.

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“…The construction of more ground-based GNSS networks and the application of more space-based GNSS observations make it possible to reconstruct the three-dimensional IED distributions. Therefore, the GNSS-based ionospheric tomography technique has attracted the interest of many scholars [7][8][9][10][11][12][13][14][15][16]. Theoretical and experimental research has been conducted, and some meaningful results have been achieved.…”

Section: Introductionmentioning

confidence: 99%

“…This fact makes it an ill-posed problem [22]. To resolve this issue, scholars have studied some tomographic algorithms in recent years [12,14,[22][23][24][25][26][27][28][29]. On the whole, these algorithms are divided into the iterative method and the non-iterative method.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smoothness Constraint Ionospheric Tomography Algorithm

Wen

Mei

2020

Sensors

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Ionospheric tomography reconstruction based on global navigation satellite system observations is usually an ill-posed problem. To resolve it, an adaptive smoothness constraint ionospheric tomography algorithm is proposed in this work. The new algorithm performs an adaptive adjustment for the constrained weight coefficients of the tomography system. The computational efficiency and the reconstructed quality of ionospheric imaging are improved by using the new algorithm. A numerical simulation experiment was conducted in order to validate the feasibility and superiority of the algorithm. The statistical results of the reconstructed errors and the comparisons of ionospheric profiles confirmed the superiority of the new algorithm. Finally, the new algorithm was successfully applied to reconstruct three-dimensional ionospheric images under geomagnetic quiet and geomagnetic disturbance conditions over Hunan province. The tomographic results are reasonable and consistent with the general behavior of the ionosphere. The positive and negative phase storm effects are found during geomagnetic storm occurrence.

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Three‐dimensional ionospheric tomography reconstruction using the model function approach in Tikhonov regularization

Cited by 17 publications

References 43 publications

A new parameterized approach for ionospheric tomography

A new parameterized approach for ionospheric tomography

Virtual reference station-based computerized ionospheric tomography

Adaptive Smoothness Constraint Ionospheric Tomography Algorithm

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