Abstract

AbstractAt present most low‐cost GNSS receivers operate one frequency in the L band. For them one of the largest error contributions is the delay of radio signals in the Ionosphere. NeQuick‐G is the official ionospheric correction algorithm (ICA), which has been adopted for Galileo, the European GNSS Programme. The NeQuick‐G implementation is complex when compared with other ICAs. It is also demanding in terms of computational resources. The Joint Research Centre completed a reference implementation of NeQuick‐G based on the official document “Ionospheric Correction Algorithm for Galileo Single Frequency Users” provided by the European Global Navigation Satellite Systems Agency. The rationale behind the JRC implementation of NeQuick‐G was the intent to write an independent source code from scratch, without using the pseudo‐codes from the reference document and solely relying on the physics descriptions. Using such implementation as baseline, this paper describes an optimization attempt of the official pseudo‐code from an algorithmic perspective. The objective was to reduce the computational load while not sacrificing the performance. The new proposed integration method is able to speed up calculations to 21% and 49% with respect the two official integration algorithms. The overall computational burden depends on the number of operations, which is eventually closely correlated to the number of calls of the ionospheric model. This underlines the quest to find an integration method reducing this number of calls. Moreover, based on the findings of this study, the authors strongly recommend revisiting the convergence control of the integration routines introduced in (European Commission, 2016, https://www.gsc‐europa.eu/sites/default/files/sites/all/files/Galileo_Ionospheric_Model.pdf).

Highlights

  • NeQuick‐G is the official Ionospheric Correction Algorithm (ICA) that has been developed for Galileo, the European GNSS Programme

  • Benchmark in the Reference Document The NeQuick‐G algorithm can be considered to be a black box with input ports for universal time, month, locations of the ray path, and three coefficients describing the state of the ionosphere (a0, a1, and a2 ionospheric coefficients that can be obtained from the Galileo navigation message) and it has a single output port that outputs the Slant Total Electron Content (STEC) as a measure of the range error along the ray path

  • This study presents an optimization attempt from an algorithmic point of view of the Galileo ionospheric correction algorithm, NeQuick‐G, performed by the Joint Research Centre (JRC)

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Summary

Introduction

NeQuick‐G is the official Ionospheric Correction Algorithm (ICA) that has been developed for Galileo, the European GNSS Programme. The implementation is available to the public at https://nequick‐g.jrc.ec. Europa.eu/ for the calculation of reference NeQuick‐G Slant Total Electron Content (STEC) and its source code is currently undergoing a licensing procedure to get a European Union Public License (European Commission, 2017) to be openly distributed. This paper provides a brief overview of the JRC implementation and presents an optimization that goes beyond the official ICA description in RD. The JRC optimization exploits the fact that NeQuick‐G fundamental mathematical task is a numerical integration. The ionospheric electron density is integrated over the Line of Sight (LoS) between a GNSS receiver and a GNSS satellite. The result of this integration is the Total Electron Content (TEC). The presented optimization is purely mathematical and the rationale to make this decision is explained in the following subsection

Profiling NeQuick‐G
NeqInterpolate
NeQuick‐G Basics
MODIP: Modified Magnetic Dip
Ionospheric Structure in NeQuick‐G
Ionospheric State
From Electronic Profile to STEC
Limitations of the Model
Overview of the JRC Implementation of NeQuick‐G
Benchmark
Assessment Criteria
Optimization Attempt
Speed and Deviation Results
Comparison Details Against the JRC Extended Benchmark
Method
Some Comments Regarding the Tested Algorithms
Lessons Learnt About Quadrature
Some Comments About Found Issues
Conclusions and Follow‐Up
Findings
Data Availability Statement
Full Text
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