## 5 Conclusions  
This study focused on ionosphere-free precise code positioning based on BDS-3/GPS/ Galileo multi-frequency pseudo-range observation data obtained with smartphones. Precise code positioning models were proposed, using Doppler smoothing pseudo-range and inter-satellite single difference methods, and the reference satellite was selected using the CNR-weighting method. As for ionospheric delay, precise ionospheric products were used for correction, or the ionosphere-free combination was applied for elimination. The IF-P model contains not only the traditional ionosphere-free combination, but also the single ionosphere-corrected pseudo-range. The IFUC model is composed of ionosphere-corrected pseudo-ranges. Real-world collected datasets from Huawei P40 and Xiaomi 11 Lite smartphones were used to validate the PCP models, and the results of a quality investigation were presented. In the static open-sky scenario, it was observed that the CNR of multi-GNSS signals was concentrated in the range of 25-40 dB-Hz. Specifically, for HP40, the CNR of the B2a/L5/E5a signal was approximately 10 dB-Hz lower than that of B1C/B1I/L1/E1, whereas for Mi11, it was approximately 6 dB-Hz lower. In the dynamic urban scenarios, the quality of the smartphones' GNSS observations was significantly degraded, characterized by low CNR values and frequent loss of satellites tracking. When comparing the static open-sky scenario to the static complex scenario and dynamic scenarios, it was observed that the number of visible satellites for HP40 remained relatively stable but was generally lower than that for Mi11. This indicates that Mi11 exhibits superior satellite observation decoding performance compared to HP40, but it lags behind HP40 in terms of satellite tracking ability. Finally, static and dynamic positioning tests were carried out. The static tests demonstrated that the PCP models can achieve sub-meter-level accuracy in the E and N directions, while achieving meter-level accuracy in the U direction. Numerically, the root mean square  
Figure 10: Positioning accuracy of dynamic scenarios. (**a**) In the typical urban scenario; (**b**) in the complex urban scenario. error (RMSE) improvement percentages were approximately 93.8%, 75%, and 82.8% for HP40 in the E, N, and U directions, respectively, in both the open-sky and complex scenarios compared to SPP. In the open-sky scenario, Mi11 showed an average increase of about 85.6%, 87%, and 16% in the E, N, and U directions, respectively, compared to SPP. In the complex scenarios, Mi11 exhibited an average increase of roughly 68%, 75.9%, and 90% in the E, N, and U directions, respectively, compared to SPP. However, in the dynamic scenarios, several factors contributed to a decrease in positioning accuracy. These factors include loose quality-control methods, complex environments, and the receiver's inability to continuously provide reference positions. As a result, in dynamic scenarios, the positioning accuracy typically ranges from 5 to 8 m in the horizontal plane and is 10 m or more in the U direction. Numerically, the PCP models only provided an improvement of approximately 10% in the horizontal plane or U direction compared to SPP. It is worth noting that the IFUC123 model exhibited the best positioning performance, while the TF-P model performed the worst, likely due to the less rigorous quality control of B2a pseudo-range data. Further research will continue to delve deeper into these challenges. In summary, the PCP models have demonstrated their capability to significantly enhance the positioning accuracy of smartphones under static scenarios. However, it is acknowledged that dynamic scenarios present unique challenges, and further in-depth research is needed to achieve high-precision positioning for smartphones in dynamic environments. Conceptualization, R.W. and C.H. ; methodology, R.W. ; software, R.W. ; validation, R.W., C.H., Z.W., F.Y. and Y.W. ; formal analysis, R.W. ; investigation, C.H. and Y.W. ; resources, C.H. and Z.W. ; data curation, C.H. and F.Y.