Triple-Frequency Combining Observation Models and Performance in Precise Point Positioning Using Real BDS Data

Although precise point positioning (PPP) is a well-established and promising technique with the use of precise satellite orbit and clock products, it requires a long convergence time to reach a centimeter-level positioning accuracy. The availability of triple-frequency observations from a modernized global navigation satellite system (GNSS) constellations makes it possible to improve performance by formulating new observation models. The contribution of this paper is to propose two new observation models using triple-frequency data. The first model (UofC3) is the triple-frequency extension of dual-frequency University of Calgary model. The second model (UofB) is a combining observation model with five-dimensional observation equations. Thereafter, by theoretically analyzing the dimension of the triple-frequency observation models, all feasible triple-frequency observation models are systematically found out. Finally, the positioning experiments with 3-h observation period using real data at four BeiDou Navigation Satellite System (BDS) Asia-Pacific distributed reference stations on the day of the year (DOY) 42-48, 2018 are conducted to compare the performance of observation models. The results show that the triple-frequency models have less convergence time than the dual-frequency models. Meanwhile, most of the triple-frequency models have approximate convergence time in an experiment. Furthermore, UofB, which is one of the most stabilized observation models as well as uncombined observation model with the triple-frequency data (UC3), has less processing time than UC3. Compared with UofB, UofC3 has less processing time with sacrificing stability. These results show the significance of the UofB and UofC3 for future PPP applications in modernized GNSS.