A New Algorithm for Ionosphere STEC Modeling through Combining Empirical Ionosphere Model with GNSS Observation Data

Li Wen, Yuan Hong, Ouyang Guangzhou, Li Zishen, Wang Ningbo, Zhou Kai

Peer Reviewed

Abstract: Ionosphere correction model is an important category of methods to tackle the problem of ionosphere delay in GNSS, especially for single-frequency and high-accuracy users. Compared with conventional ionosphere TEC modeling algorithm, direct STEC modeling will avoid the systematic error introduced by the SLM assumption and STEC/VTEC conversion, so as to further improve TEC modeling accuracy. Moreover, in order to take full advantage of both empirical ionosphere model and ionosphere model obtained from GNSS observation data, a new STEC modeling algorithm is proposed in this paper with empirical model adopted as background during STEC inversion from observation data. Therefore, the new algorithm is more robust and reliable in case that there is gross error, data missing, or uneven distribution in GNSS observation data. For validation and evaluation of the new approach, IRI2012 and NeQuick2 are selected as representative background empirical models respectively and observation data from IGS tracking stations are used. Through analysis of the STEC residuals between STEC observation value and computed STEC value from the model, current test results show that the rate of STEC correction is quite satisfactory, about 90% for both IRI and NeQuick2 with the proposed algorithm in this paper. So this approach can serve as a reference for ionosphere product generation in wide-area augmentation systems.
Published in: Proceedings of the ION 2017 Pacific PNT Meeting
May 1 - 4, 2017
Marriott Waikiki Beach Resort & Spa
Honolulu, Hawaii
Pages: 670 - 686
Cite this article: Wen, Li, Hong, Yuan, Guangzhou, Ouyang, Zishen, Li, Ningbo, Wang, Kai, Zhou, "A New Algorithm for Ionosphere STEC Modeling through Combining Empirical Ionosphere Model with GNSS Observation Data," Proceedings of the ION 2017 Pacific PNT Meeting, Honolulu, Hawaii, May 2017, pp. 670-686. https://doi.org/10.33012/2017.15100
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