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An adaptive Kalman filter based on variance component estimation for a real-time ZTD solution

  • Xu Yang
  • Guobin Chang
  • Qianxin WangEmail author
  • Shubi Zhang
  • Ya Mao
  • Xiongchuan Chen
Original Study
  • 21 Downloads

Abstract

The Global Navigation Satellite System (GNSS) precise point positioning (PPP) technology is currently used to process GNSS water vapor observations in real time or near real time. Further developments are required to improve the accuracy and real-time performance of processing tropospheric delays from which the water vapor observations are extracted. In real-time BDS/GPS precise clock correction estimation with square-root information filtering and PPP solution with Kalman filtering, a fixed variance is often assigned for the process noise of the troposphere dynamics model. However, this fixed value may deviate from reality as the weather conditions change, especially for extreme weather. In this paper, a new adaptive Kalman filter is proposed for tropospheric delay processing, in which the variance of the process noise for the zenith tropospheric delay (ZTD) dynamics model is tuned in real time using the least-squares variance component estimation technique. MGEX/IGS data of 15 consecutive days were processed in a stepwise manner. Namely: Real-time BDS/GPS precise clock corrections were estimated firstly, followed by comparison among ZTD solutions of four schemes based on these real-time clocks of first step and GFZ multi-GNSS precise clock (GBM) final clocks: (1) real-time solution of ZTD (G(GPS), GC(GPS and BDS)) without VCE; (2) real-time solution of ZTD (G, GC) with VCE; (3) final solution of ZTD (G, GC, GR (GPS and GLONASS), GE (GPS and GALILEO), GRC (GPS, GLONASS and BDS), GREC (GPS, GLONASS, GALILEO and BDS) without VCE; and (4) final solution of ZTD (G, GC, GR, GE, GRC, GREC) with VCE. The performance of the ZTD and positioning solution was analyzed. Results showed that the accuracy of estimated real-time satellite clock correction was 0.27, 1.31, 0.29, and 0.21 ns for GPS, BDS/GEO, BDS/IGSO, and BDS/MEO, respectively. For the ZTD solutions, the results of schemes 1 and 2 were 12.8 mm (GC) and 10.1 mm (GC) in terms of mean root-mean-square (RMS) values and 2.1 mm (GC) and 1.6 mm (GC) in terms of minimum RMS values, respectively, thereby showing an improvement for scheme 2 of 1.8–81.4% over scheme 1 with average increasing rates of 20.7% (GC) and 20.2% (G). The results for schemes 3 and 4 were 7.6 mm (G) and 6.3 mm (GRC) in terms of mean RMS values and 2.1 mm (G) and 1.9 mm (GRCE) in terms of minimum RMS values, respectively, thereby showing an improvement in scheme 4 over scheme 3 by 1.3–85.6% with average increasing rates of 22.1% (GRCE), 21.9% (GRC), 18.4% (GR), 15.9% (GC), 15.2% (GE), and 12.1% (G). Similar results can be observed for the positioning solution, especially in the height component. These findings clearly show the advantage of the proposed method, which is consistent with the theoretical analysis. Notably, the advantage of the adaptive VCE becomes significant with the inclusion of additional satellite systems.

Keywords

GNSS Zenith tropospheric delay (ZTD) Variance component estimation Adaptive Kalman filter Real-time clock estimation 

Notes

Acknowledgments

This work was supported by “The National Science and Technology Basic Work of China (No. 2015FY310200)”, “The State Key Program of National Natural Science Foundation of China (No. 41730109)”, “The National Natural Science Foundation of China (Grant Nos: 41404033, 41874039, 41774026,41774005, 51374209 and 41604006)”, “The Jiangsu Dual Creative Teams Program Project Awarded in 2017”, “Jiangsu Natural Science Foundation (Grant No. BK20181361)” and thanks for the data from MEGX/IGS and iGMAS.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Akadémiai Kiadó 2019

Authors and Affiliations

  1. 1.NASG Key Laboratory of Land Environment and Disaster MonitoringChina University of Mining and TechnologyXuzhouChina
  2. 2.School of Environment Science and Spatial InformaticsChina University of Mining and TechnologyXuzhouChina
  3. 3.Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, School of Science, Mathematical and Geospatial SciencesRMIT UniversityMelbourneAustralia

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