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Automatic Calibration of Process Noise Matrix and Measurement Noise Covariance for Multi-GNSS Precise Point Positioning

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  • Xinggang Zhang

    (National Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, China
    Key Laboratory of Precision Navigation Positioning and Timing Technology, Chinese Academy of Sciences, Xi’an 710600, China
    German Research Centre for Geosciences (GFZ), 14473 Potsdam, Germany)

  • Pan Li

    (German Research Centre for Geosciences (GFZ), 14473 Potsdam, Germany)

  • Rui Tu

    (National Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, China
    Key Laboratory of Precision Navigation Positioning and Timing Technology, Chinese Academy of Sciences, Xi’an 710600, China)

  • Xiaochun Lu

    (National Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, China
    Key Laboratory of Precision Navigation Positioning and Timing Technology, Chinese Academy of Sciences, Xi’an 710600, China)

  • Maorong Ge

    (German Research Centre for Geosciences (GFZ), 14473 Potsdam, Germany
    Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany)

  • Harald Schuh

    (German Research Centre for Geosciences (GFZ), 14473 Potsdam, Germany
    Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany)

Abstract

The Expectation-Maximization algorithm is adapted to the extended Kalman filter to multiple GNSS Precise Point Positioning (PPP), named EM-PPP. EM-PPP considers better the compatibility of multiple GNSS data processing and characteristics of receiver motion, targeting to calibrate the process noise matrix Q t and observation matrix R t , having influence on PPP convergence time and precision, with other parameters. It is possibly a feasible way to estimate a large number of parameters to a certain extent for its simplicity and easy implementation. We also compare EM-algorithm with other methods like least-squares (co)variance component estimation (LS-VCE), maximum likelihood estimation (MLE), showing that EM-algorithm from restricted maximum likelihood (REML) will be identical to LS-VCE if certain weight matrix is chosen for LS-VCE. To assess the performance of the approach, daily observations from a network of 14 globally distributed International GNSS Service (IGS) multi-GNSS stations were processed using ionosphere-free combinations. The stations were assumed to be in kinematic motion with initial random walk noise of 1 mm every 30 s. The initial standard deviations for ionosphere-free code and carrier phase measurements are set to 3 m and 0.03 m, respectively, independent of the satellite elevation angle. It is shown that the calibrated R t agrees well with observation residuals, reflecting effects of the accuracy of different satellite precise product and receiver-satellite geometry variations, and effectively resisting outliers. The calibrated Q t converges to its true value after about 50 iterations in our case. A kinematic test was also performed to derive 1 Hz GPS displacements, showing the RMSs and STDs w.r.t. real-time kinematic (RTK) are improved and the proper Q t is found out at the same time. According to our analysis despite the criticism that EM-PPP is very time-consuming because a large number of parameters are calculated and the first-order convergence of EM-algorithm, it is a numerically stable and simple approach to consider the temporal nature of state-space model of PPP, in particular when Q t and R t are not known well, its performance without fixing ambiguities can even parallel to traditional PPP-RTK.

Suggested Citation

  • Xinggang Zhang & Pan Li & Rui Tu & Xiaochun Lu & Maorong Ge & Harald Schuh, 2020. "Automatic Calibration of Process Noise Matrix and Measurement Noise Covariance for Multi-GNSS Precise Point Positioning," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:502-:d:340475
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    References listed on IDEAS

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    1. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
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