## 5 Conclusions  
In this study, we proposed an IMU-aided uncombined PPP coupled mathematical model that integrated IMU data and GNSS observations from smartphones to enhance the positioning performance of smartphones under GNSS-degraded environments. The GNSS measurements observed by the dual-frequency smartphone Huawei Mate 30 integrated Hi1103 chipset and the single-frequency smartphone Huawei Mate 10 integrated BCM47531 chipset, and the data of IMU from the smartphones and triaxial inertial sensor ADIS16405, were used in several experiments. The main conclusions are summarized as follows:  
1. More than half of the carrier phase measurements cannot be observed in dynamic mode by the early single-frequency smartphones such as the Mate 10 in some areas where GNSS signal degrades slightly. Although the positioning accuracy of the Mate 10 is improved with the PPP/INS coupled model, its absolute positioning accuracy is still closely related to PPP accuracy. The weak positioning performance of single-frequency smartphones makes it difficult to meet the requirements of a precision location service, even in the open area. 2. Unlike the geodetic receiver, the number of visible GNSS satellites observed by the Mate 30 fluctuate within a narrow range, which is caused by the weak multipath suppression ability of GNSS antenna of smartphones. In some areas where GNSS signals are significantly degraded due to the buildings and trees on both sides, the RMS of smartphone PPP horizontal positioning errors can even reach more than 10 m.
3. With the proposed PPP/INS coupled method, the RMS of PPP horizontal and vertical positioning errors on smartphone decreased significantly in various GNSS-degraded environments, and long trajectory experimental results indicated that the RMS of PPP/INS horizontal errors in the eastern and western areas decrease by 49.37% and 48.29%, respectively, compared with convention PPP solutions. Meanwhile, the results of several experiments also show that the positioning accuracy of PPP/INS is roughly equivalent to that of PPP/INS-ADIS. 4. Moreover, the position of smartphone can be outputted continuously while moving through the tunnel with a length of about 80 m; the PPP/INS trajectory deviation of smartphone in the tunnel remained stable. The horizontal deviation from the reference point is more than 8 m after 29 epochs, and the positioning points quickly approach the reference trajectory after leaving the tunnel. In summary, the proposed PPP/INS coupled model integrated dual-frequency GNSS observations and IMU data from smartphones with C/N\({}_{0}\)-dependent stochastic model and RKF model to improve the positioning performance in GNSS-degraded environments. However, the absolute positioning accuracy of GNSS PPP/INS in the open area is still closely related to PPP accuracy. Moreover, in the northern area of long trajectory experiments, the deviation of PPP/INS positioning gradually increases when the GNSS signals are blocked for a long time, which is related to the positioning error before entering the GNSS-signals-blocked area and the drift of INS. Therefore, we will further integrate other sensors such as cameras in smartphones for when the GNSS signals are blocked for long period of time, and conduct the experiments with multiple modes of transportation. In addition, with the improved performance of GNSS and internal IMU chipset of smartphones in the future, we can anticipate that high-precision position of smartphones will play a significant part in various navigation applications such as ITS and LBS. Conceptualization, H.Z. and L.X. ; methodology, H.Z. and Q.L. ; software, H.Z. ; validation, H.Z., Q.L., J.X. and Y.C. ; writing--original draft preparation, H.Z. ; writing--review and editing, H.Z. and L.X. ; visualization, H.Z. ; supervision, L.X. ; project administration, L.X. All authors have read and agreed to the published version of the manuscript.