# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 5 Discussion  
Serving as moving stations, the LEO satellites can significantly strengthen the tracking geometric diversity of GNSS satellites and improve the accuracy of the GNSS precise orbit determination, particularly in the case of regional network or sparse global network. Although many studies have reported the contribution of LEO onboard observations to the GNSS POD, few studies have focused on the performance of the integrated POD with a large LEO constellation. Meanwhile, the impact factors of the integrated POD, such as LEO number, orbital height, and orbit type, is barely analyzed. In this study, we presented the GNSS orbit solution derived in an integrated processing of the ground network and the large LEO constellation using simulated data. Six LEO constellations are designed to study the performance of the integrated POD with different tracking configurations. The results demonstrate that the orbit accuracy of GNSS satellites can be dramatically improved when a large number of LEO satellites is introduced; the more LEO satellites, the better the orbit accuracy. Notably, BDS GEO orbits present an accuracy of a few centimeters with the recruitment of LEO satellites. Moreover, the LEO satellite in sun-synchronous orbit can contribute more to GNSS orbits than nearly polar-orbiting satellites, since they give rise to a stronger observation geometry. Furthermore, the orbit altitude of LEO satellites is found to have no evident impact on the GNSS satellites except BDS GEO satellites. In addition, it can be seen that the integrated POD with the entire LEO constellation suffers from a long computational time because of the abundant GNSS observations to be processed and the large number of parameters to be estimated. The long computation time of the integrated POD cannot meet the  
Figure 13: Number of estimated parameters and computation time of the integrated POD with LEO satellites in nearly-polar orbit. timeliness requirement of the real-time precise positioning. This weakness in terms of computational efficiency can be overcome by just introducing a certain number of LEO satellites. Although our study presents an optimistic integrated POD result due to the simulated data, the impact of introducing LEO constellations to improve GNSS orbit determination is clearly demonstrated. Indeed, not only tracking geometry, but many other factors, such as attitude model, solar radiation pressure and drag models, and antenna calibrations, constitute key limitations in real-world LEO and GNSS POD. Fortunately, with the efforts of many scientists, the force and observation models have been rigorously refined in recent years, which can reduce the impact of model errors on the GNSS POD as much as possible. Using the current models, GNSS satellites (except BDS GEO) can achieve centimeter-level orbits [1], though more efforts are needed to further refine the force and observation models. Meanwhile, the force model errors do not represent a weakness in the observability and tracking geometry, which is a separate issue from the tracking geometry that needs to be further investigated. In addition, except for the factors mentioned above, there are still many issues which need to be taken into consideration when implementing the integrated POD using real GNSS observations. For example, the incorporation of a large number of real multi-GNSS onboard observations may introduce unexpected unmodeled errors into the orbit recovery process. Meanwhile, different from ground receivers, the performance of space-borne receivers can be more easily affected by the space environment due to the high altitude, i.e., 600 km\(\sim\)1000 km, where LEO satellites are flying. As discussed in [PERSON] et al. [39], large equatorial plasma irregularities can degrade the tracking capability of the Swarm onboard receivers, leading to severe signal loss at low latitudes. This loss of signal for space-borne receivers undoubtedly has negative effects on the integrated POD.