章繁, 柴洪洲, 王敏, 肖国锐, 张乾坤, 杜祯强. 组合GPS/GLONASS三频观测值的非差非组合PPP模糊度快速固定[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220315
引用本文: 章繁, 柴洪洲, 王敏, 肖国锐, 张乾坤, 杜祯强. 组合GPS/GLONASS三频观测值的非差非组合PPP模糊度快速固定[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220315
Fan Zhang, Hongzhou Chai, Min Wang, Guorui Xiao, Qiankun Zhang, Zhenqiang Du. Uncombined PPP Ambiguity Resolution Combined with GPS/GLONASS Triple-frequency Observations[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220315
Citation: Fan Zhang, Hongzhou Chai, Min Wang, Guorui Xiao, Qiankun Zhang, Zhenqiang Du. Uncombined PPP Ambiguity Resolution Combined with GPS/GLONASS Triple-frequency Observations[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220315

组合GPS/GLONASS三频观测值的非差非组合PPP模糊度快速固定

Uncombined PPP Ambiguity Resolution Combined with GPS/GLONASS Triple-frequency Observations

  • Abstract: Objectives:    Currently, two GLONASS-K1 and four GLONASS-M+ satellites transmit Code Division Multiple Access (CDMA) signals on the third frequency in addition to the traditional FDMA signals on the first two frequencies, making it possible for GLONASS joint use of Frequency Division Multiple Access (FDMA) and CDMA signals for precise point positioning (PPP). To follow with the trend of multi-frequency and multi-system, GPS+GLONASS triple-frequency uncombined PPP ambiguity resolution model is investigated.    Methods:    A GPS+GLONASS triple-frequency uncombined PPP model considering the inter-frequency clock bias (IFCB) of GPS and GLONASS systems is presented first. Then we presented the triple-frequency uncalibrated phase delay (UPD) estimation method and our PPP-AR algorithm. However, limited to the number of GLONASS triple-frequency observations and its distribution, we combine GPS and GLONASS triple-frequency observations for PPP-AR, but only fix GPS triple-frequency ambiguities, while GLONASS ambiguity maintains its floating-point form. The IFCB and UPD products are estimated by more than 300 MGEX stations distributed around the world, and another 14 MGEX stations are used to evaluate our PPP-AR performance.    Results:    Experimental results demonstrate that the best positioning performance has been achieved by our algorithm. Compared with the GPS triple-frequency PPP-AR, the positioning accuracies of the static experiments in the east, north, up and 3D directions are improved by 80.7%, 60.0%, 61.0% and 63.8% respectively, and that of the simulated dynamic experiments are improved by 41.9%, 14.5%, 11.6% and 16.3%. However, if GLONASS IFCB errors are ignored, the stability of the convergence series of the combined triple-frequency PPP-AR will be harmed, causing the positioning performance even inferior to the GPS single system. Moreover, our algorithm achieves rapid ambiguity resolutions. The average time to first fix (TTFF) time of static and simulated dynamic experiments of our model is about 12.3 and 12.9 minutes respectively, which is significantly improved compared with the GPS, in which the improvement of the simulated dynamic experiment is 53.8%. Besides, compared with the scheme of combining GPS triple-frequency observations and GLONASS dual-frequency observations, after integrating GLONASS CDMA observations, the stability of the positioning time series of the simulated dynamic experiments is effectively improved, and the RMS value of positioning errors in each direction can be reduced.    Conclusions:    These results demonstrate that the best positioning performance can be achieved by our proposed GPS+GLONASS triple-frequency PPP-AR. Moreover, GLONASS IFCB errors must be carefully investigated and calibrated beforehand. However, limited by the number of GLONASS CDMA datasets and other factors, it is difficult to perform GLONASS triple-frequency PPP-AR with FDMA and CDMA signals currently. In the future work, we will focus on the investigation of GLONASS triple-frequency PPP-AR.

     

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