Adaptive Smoothness Constraint Ionospheric Tomography Algorithm

Abstract

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.

Electron Density Reconstruction by Ionospheric Tomography From the Combination of GNSS and Upcoming LEO Constellations

“…In this study, the GNSS‐based and LEO‐based STEC observations are simulated for experiments with maxima and minima solar activity. The ionospheric electron density of each voxel is estimated based on the simulated STEC values by using the multiplicative algebraic reconstruction technique (MART), which is commonly used in ionospheric tomography for its nonnegative results and a fast iteration (dos Santos Prol and de Oliveira Camargo, 2016; Wen et al., 2020). The iteration formula of MART algorithm is usually given as follows (Bender et al., 2011): …”

The Potential of LEO-PNT Mega-Constellations for Ionospheric 3-D Imaging: A Simulation Study

“…Like in classic GNSS [21], dedicated LEO-PNT systems will likely provide carrier-phase and pseudorange measurements to users on the ground, which can be rather used to compute the total electron content (TEC) and generate valuable representations of the ionosphere in 2-D. A major drawback in the ionospheric imaging based on GNSS, however, is the incomplete geometrical coverage of the GNSS ray paths to estimate the vertical distribution of the ionosphere in 3-D representations [22], [23]. Despite the progress in the development of ionospheric tomography [24], [25], [26], [27], data ingestion [28], and data assimilation [29], [30], 3-D ionospheric imaging is currently an ill-conditioned and ill-posed inverse problem due to the GNSS poor geometry [31]. With the expected better geometry coverage by LEO satellites, upcoming LEO-PNT systems can likely provide gains in the 3-D ionospheric imaging.…”