Least‐squares collocation modelling of regional ionospheric TEC for accelerating real‐time single‐frequency PPP convergence. Issue 6 (1st June 2019)

• Record Type: Journal Article
• Authors: Gong, Yangzhao; Cai, Changsheng; Dai, Wujiao; Zhu, Jianjun
• Is Part Of: IET radar, sonar & navigation. Volume 13:Issue 6(2019)
• Page Start: 1031
• Page End: 1038
• Publication Date: 2019-06-01
• Subjects: ionosphere -- polynomials -- ionospheric electromagnetic wave propagation -- Global Positioning System -- total electron content (atmosphere) -- ionospheric techniques -- radiowave propagation; random variable part -- TEC's irregular variations -- regional ionospheric TEC model -- least‐squares collocation approach -- ionospheric TEC variations -- predicted TEC -- LSC RIM -- real‐time SF PPP -- different latitude regions -- LSC model -- conventional polynomial model -- gGlobal ionosphere map‐predicted products -- GIM‐predicted products -- ionospheric delay errors -- middle latitude region -- low latitude region -- GPS RT SF PPP -- convergence time -- squares collocation modelling -- real‐time single‐frequency PPP convergence -- regional ionospheric total electron content -- single‐frequency precise point positioning -- existing regional ionospheric models; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5504
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Modelling of the regional ionospheric total electron content (TEC) is an important means for mitigating the ionospheric delay errors in the single‐frequency (SF) precise point positioning (PPP). Mostly, the existing regional ionospheric models (RIM) only takes the trend term of the ionospheric TEC variations into account and neglect their random variable part. As a result, the TEC's irregular variations cannot be well modelled. In this study, a regional ionospheric TEC model based on the least‐squares collocation (LSC) approach is proposed to account for both the trend and random parts. The predicted TEC from the LSC RIM is then employed in the real‐time (RT) SF PPP for speeding up convergence. Datasets collected in two different latitude regions are utilised to establish the LSC RIM with a comparison to the conventional polynomial model and the global ionosphere map (GIM)‐predicted products. The results show that the LSC model can remove ionospheric delay errors of at least 92%, which is larger than the polynomial model and GIM products. Further, the GPS RT SF PPP after introducing the predicted TEC from the LSC RIM can significantly reduce the convergence time by at least 48%, 58%, and 5% in the east, north, and up directions.