Abstract

Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

Highlights

  • In safety-of-life (SoL) navigation applications using Global Navigation Satellite Systems (GNSS), the main performance parameter of interest is integrity

  • The most common tropospheric delay models can operate in a so-called site augmented mode, where the user can supply in situ meteorological data, as mentioned before, this study focuses on the blind mode of operation, in which such data is not used and meteorological parameters are derived by the receiver as a function of time and location

  • We proposed a new tropospheric residual error model derived from ray tracing numerical weather model data over a period of 17 years

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Summary

Introduction

In safety-of-life (SoL) navigation applications using Global Navigation Satellite Systems (GNSS), the main performance parameter of interest is integrity. To apply the extreme value theory for the estimation of maximum residual errors, we normalized the data set by an appropriate time-dependent function, which describes the seasonal variation of the standard deviation of the daily residuals.

Results
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