International Global Navigation Satellite Systems Association
IGNSS Symposium 2018
Colombo Theatres, Kensington Campus, UNSW Australia
7 – 9 February 2018
Analysis of Double-Differenced Multi-GNSS Inter-
System Biases for Overlapping and
Mixed Frequencies
Shuyang Cheng, Jinling Wang and Wenhao Zhang
School of Civil and Environmental Engineering, University of New South Wales,
Sydney, NSW 2052, Australia
Phone: +61 416 866 545 Fax: +61 2 9385 6139 Email: shuy ang.c heng@student.unsw.edu.au
ABSTRACT
Compatibility and interoperability among multi-GNSS constellations are
necessary for reliable multi-GNSS positioning and navigation, thus,
attracting great attention from the satellite navigation community over recent
years. It has been well known that Double-Differenced (DD) integer
ambiguities between satellites in the same constellation are resolvable. If the
integer carrier phase ambiguities between satellites in different constellations
are to be resolved, multi-GNSS Inter-System Bias (ISB) should be taken into
consideration. Most of current studies focus on ISB estimation and statistical
analysis for overlapping frequencies of different constellations, such as GPS-
Galileo (L1-E1, L5-E5a) or Galileo-BDS (E5b-B2). ISB estimation for
mixed frequencies is still challenging and rarely investigated.
In this paper, we will design procedures to estimate multi-GNSS code and
carrier phase ISBs towards the goal to resolve the DD integer ambiguities
between any satellites within the multi-constellation GNSS. In addition, the
statistical characteristics of these biases for both overlapping and mixed
frequencies will be analysed. Data from several ultra-short and zero
baselines are used to estimate the DD ISBs for three types of commercial
GNSS receivers. Experimental results indicate that for the baselines with the
same receiver type, code and phase ISBs have long-term stability for both
overlapping and mixed frequencies. Although the code and phase ISBs may
have small variations for the baselines with different receiver types, these
variations are very slow and smooth, so they can be properly modelled.
KEYWORDS: Inter-System Biases, Multi-GNSS, Inter-System Double-
Differencing, Statistical Analysis
