Two‐dimensional ionospheric tomography over the low‐latitude Indian region: An intercomparison of ART and MART algorithms

Abstract

[1] Single-frequency users of a satellite-based augmentation system (SBAS) rely on ionospheric models to mitigate the delay due to the ionosphere. The ionosphere is the major source of range and range rate errors for users of the Global Positioning System (GPS) who require high-accuracy positioning. The purpose of the present study is to develop a tomography model to reconstruct the total electron content (TEC) over the low-latitude Indian region which lies in the equatorial ionospheric anomaly belt. In the present study, the TEC data collected from the six TEC collection stations along a longitudinal belt of around 77 degrees are used. The main objective of the study is to find out optimum pixel size which supports a better reconstruction of the electron density and hence the TEC over the low-latitude Indian region. Performance of two reconstruction algorithms Algebraic Reconstruction Technique (ART) and Multiplicative Algebraic Reconstruction Technique (MART) is analyzed for different pixel sizes varying from 1 to 6 degrees in latitude. It is found from the analysis that the optimum pixel size is 5°× 50 km over the Indian region using both ART and MART algorithms.Citation: Das, S. K., and A. K. Shukla (2011), Two-dimensional ionospheric tomography over the low-latitude Indian region: An intercomparison of ART and MART algorithms, Radio Sci., 46, RS2005,

Numerical Simulations to Assess ART and MART Performance for Ionospheric Tomography of Chapman Profiles

“…As can be seen in Figure 7 and similar to the analysis made by Das and Shukla (2011), MART performs better in estimating n m . When using n m derived from the image to retrieve TEC observations as reference, we obtained the absolute mean of the discrepancy of MART equal to 0.67 10 11 el/m³ for Case I and 0.28 10 11 el/m³ for Case II.…”

“…They used a simulated scenario to accurately reconstruct the ionosphere using MART and showed that the reconstruction was highly dependent on the initial guess of the ionosphere. Since then, many other simulations have been conducted to analyze the efficiency of ionospheric tomographic algorithms (Raymund et al 1994, Howe et al 1998, Mitchell and Spencer 2003, Thampi et al 2004, Materassi and Mitchell 2005a, b, Wen et al 2012, Chartier et al 2014, Seemala et al 2014 and for making comparison between ART and MART algorithms for ionospheric studies (Das and Shukla 2011). However, in these simulated studies, the analysis of the efficiency of the algorithms uses climatological models to simulate the ionospheric morphology and/or depends on the real scenario of existing ground-based GNSS receivers used to retrieve the simulated observations of TEC.…”

Comparisons of ionospheric electron density distributions reconstructed by GPS computerized tomography, backscatter ionograms, and vertical ionograms

“…The MART algorithm requires initial guess of the ionospheric ionospheric electron density (IED) to be reconstructed. This initialization represents a gross estimate or a guess of what the reconstruction results might look like [ Das and Shukla , ]. The iteration of the reconstruction algorithm acts to correct this guess toward a satisfactory solution.…”

Comparative Study of Methods for Calculating Ionospheric Points and Describing the GNSS Signal Path

“…Since it is a simple formulation, low computational efforts are required, which makes it an interesting formulation for many ionospheric systems that requires an intense data process. Das and Shukla (2011) and Prol and Camargo (2016), for example, use such formulation for describing the entire GNSS signal path and performing ionospheric tomographic reconstruction. However, this simplified solution requires a few geometric approximations and, therefore, intrinsic errors in the calculation of the ionospheric points are unavoidable.…”