An improved method using adaptive smoothing for GNSS tomographic imaging of ionosphere

Rushang Jia, Xumin Yu, Jianping Xing, Yafei Ning and Hecheng Sun

PLOS ONE, 2021, vol. 16, issue 5, 1-19

Abstract: Global navigation satellite system (GNSS) is a well-established sensors in the recent ionosphere research. By comparing with classical meteorological equipments, the GNSS application can obtain more reliable and precious ionospheric total electron content (TEC) result. However, the most used GNSS ionospheric tomography technique is sensitive to a priori information due to the sparse and non-uniform distribution of GNSS stations. In this paper, we propose an improved method based on adaptive Laplacian smoothing and algebraic reconstruction technique (ALS-ART). Compared with traditional constant constraints, this method is less dependent on a priori information and adaptive smoothing constraints is closer to the actual situation. Tomography experiments using simulated data show that reconstruction accuracy of ionospheric electron density using ALS-ART method is significantly improved. We also use the method to do the analysis of real observation data and compare the tomography results with ionosonde observation data. The results demonstrate the superiority and reliability of the proposed method compared to traditional constant constraints method which will further improve the capability of obtaining precious ionosphere TEC by using GNSS.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0250613

DOI: 10.1371/journal.pone.0250613

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