Network real-time kinematic data screening by means of multivariate statistical analysis

Abstract

We introduce a novel approach to the computation of network real-time kinematic (NRTK) data integrity, which can be used to improve the position accuracy for a rover receiver in the field. Our approach is based on multivariate statistical analysis and stochastic generalized linear model (SGLM). The new approach has an important objective of alarming GNSS network RTK carrier-phase users in case of an error by introducing a multi-layered approach. The network average error corrections and the corresponding variance fields are computed from the data, while the squared Mahalanobis distance (SMD) and Mahalanobis depth (MD) are used as test statistics to detect and remove data from satellites that supply inaccurate data. The variance-covariance matrices are also inspected and monitored to avoid the Heywood effect, i.e. negative variance generated by the processing filters. The quality checks were carried out at both the system and user levels in order to reduce the impact of extreme events on the rover position estimates. The SGLM is used to predict the user carrier-phase and code error statistics. Finally, we present analyses of real-world data sets to establish the practical viability of the proposed methods.

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“…However, during heavy rainfall or other severe weather conditions, tropospheric anomalies may occur, causing high spatial variation [ 3 , 4 ] of the tropospheric delay that cannot be easily mitigated, resulting in residual errors that can affect the navigation system’s accuracy and integrity. This is of particular concern for both the existing GNSS augmentation techniques, such as ground-based augmentation systems (GBAS) [ 5 , 6 ] that rely on correction generation based on local observations and modeling the effect on the user side, and the evolving high precision GNSS augmentation systems, such as the network real time kinematic (NRTK) implementations [ 7 , 8 ] aiming at integrity support to cover the needs of new user groups, e.g., in future autonomous transportation operations where the observed residuals are interpolated to the position of the user or a virtual reference station (VRS).…”

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“…Network RTK (NRTK) techniques use a network of reference stations to provide a resolution in real time. These techniques collect raw measurements from the network of reference stations, solve the ambiguities within the reference network, generate error estimates, and then apply an interpolation scheme to generate the NRTK corrections for the user location . Several NRTK solutions are used to generate NRTK corrections for rover users, such as virtual reference station (VRS), multi reference station (MRS), master auxiliary concept (MAC), Flächen‐korrektur‐parameter (FKP), individualised master‐auxiliary (i‐MAX), and network adjustment (NetAdjust) .…”

“…These techniques collect raw measurements from the network of reference stations, solve the ambiguities within the reference network, generate error estimates, and then apply an interpolation scheme to generate the NRTK corrections for the user location. 33 Several NRTK solutions are used to generate NRTK corrections for rover users, such as virtual reference station (VRS), 32,34 multi reference station (MRS), [35][36][37] master auxiliary concept (MAC), 38 Flächen-korrektur-parameter (FKP), 31 individualised master-auxiliary (i-MAX), 39 and network adjustment (NetAdjust). 40 Takac and Zelzer 41 examine the relationship between some of these techniques aiming to improve estimation of the quality of the network correction information performed by the rover.…”