Initial Assessment of Galileo Triple-Frequency Ambiguity Resolution between Reference Stations in the Hong Kong Area

• Main Authors: Yangyang Li, Mingxing Shen, Lei Yang, Chenlong Deng, Weiming Tang, Xuan Zou, Yawei Wang, Yongfeng Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Remote Sensing
• Subjects: Galileo; ionospheric-free triple-carrier ambiguity resolution (IF TCAR); geometry-free triple-carrier ambiguity resolution (GF TCAR); combined IF TCAR (CIF TCAR)
• Online Access: https://www.mdpi.com/2072-4292/13/4/778

The European Global Navigation Satellite System Galileo is gradually deploying its constellation. In order to provide reliable navigation and position services, the effectiveness and reliability of ambiguity resolution between reference stations is necessary in network real-time kinematic (NRTK). The multifrequency signal of Galileo could much enhance the ambiguity resolution (AR) reliability and robustness. In this study, to exploit full advantage of this, the geometry-free (GF) TCAR and ionospheric-free (IF) triple-carrier ambiguity resolution (TCAR) methods were utilized in solving the ambiguity in the Hong Kong area, which is an ionosphere disturbance active area, and compared with each other. The IF TCAR method was then used to combine multi-systems to improve Galileo E1 AR performance, which is named as the combined IF (CIF) TCAR method. Three experiments were carried out in the Hong Kong area and the results showed that the Galileo-only system could fix ambiguities on all satellite pairs correctly and reliably by the IF TCAR method, while the GF TCAR method showed a weaker performance. The wide-lane (WL) convergence time of the IF TCAR method is improved by about 37.6%. The IF TCAR method with respect to the GF TCAR method could improve the WL accuracy by 21.6% and the E1 accuracy by 72.7%, respectively. Compared with GPS-only TCAR or Galileo-only TCAR, the ambiguity accuracy and the convergence time of the CIF TCAR method, which combines GPS and Galileo, could be improved by about 25.7% and 47.1%, respectively.