Abstract

Most GPS users employ low cost receivers. These receivers do not allow users to record the pseudorange that they observe, but the computed coordinates. This work presents an original and simple method to correct ionos- pheric biases introduced in GPS signals. The originality of this method is based on the fact that no pseudorange is needed to correct the biases, only the calculated coordinates are used. This distinguishes this method from oth- er classic alternatives. This paper evaluates the efficiency of the method with the use of real data.

Highlights

  • The Global Positioning System (GPS) is the most developed and extended Global Navigation Satellite System (GNSS)

  • Results show that the proposed methodology is capable of improving GPS positioning accuracy even when no pseudoranges are recorded

  • The method presented in this work improves the accuracy of positioning with low cost GPS equipment without any extra expense

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Summary

Introduction

The Global Positioning System (GPS) is the most developed and extended Global Navigation Satellite System (GNSS). It is well known that the accuracy of GPS positioning is degraded by several biases (Seeber, 2003) Some of these biases are: delays caused by the atmosphere (ionosphere and troposphere), satellite clock and orbit inaccuracy and spurious signal reflection (multi-path). In order to mitigate the ionospheric bias effect on positioning, GPS receivers use an embedded ionospheric model called Ionospheric Correction Algorithm (ICA) (Klobuchar, 1987). The GPS user community has developed several alternative methods to correct this effect. This paper will present a post-processing method to mitigate the effect of the ionospheric bias on positioning that does not require the observed pseudorange but only the user coordinates and the Pseudo Random Number (PRN) of the observed satellites. The method does not depend on the ionospheric model used and it accepts corrections from any algorithm that models the ionosphere

Low precision positioning techniques
Stand-alone positioning
Precise post-processing point positioning
Comparison between the three techniques
Algorithm to mitigate the effect of the ionospheric bias on positioning
Error propagation into coordinates
Method to correct ionospheric biases at coordinate level
Data set
Algorithm performance
Conclusions

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