## 5 Conclusions  
Real-time positioning using raw GNSS measurements of smartphones is a new research topic in GNSS navigation. In this study, we used Samsung Galaxy S21 Ultra and Xiaomi Mi 8 smartphones to assess the conventional method's accuracy and enhance the positioning accuracy using the INS fusion approach. Both smartphones can log code the pseudorange and carrier phase and Doppler measurements in two frequencies (L1/E1 and L5/E5) from the GPS, GLONASS, Galileo, BeiDou, and QZSS systems. Furthermore, a U-Blox GNSS receiver, model ZED-F9R, was used to compare the positions obtained from the smartphones using different approaches. Our method was based on the robust Kalman filter integration of the INS and raw GNSS measurements of the smartphones in the RTK mode. In urban areas with short or low-rise buildings, the RMS values were as low as the sub-meter level, i.e., 0.380 m for the Xiaomi Mi 8 smartphone and 0.415 m for the Samsung Galaxy S21 Ultra. However, in the worst case with complex urban environments and high-rise buildings, the RMS values for the Xiaomi Mi 8 and the Samsung Galaxy S21 Ultra smartphones increased to 1.518 m and 2.445 m, respectively. Based on the results, both the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 show the best performance with the RTK mode, which is known for its high accuracy in open-sky conditions and is often used in surveying and applications requiring high precision. The SPP mode has the largest errors, which is typical for urban environments where GNSS signals can be obstructed or reflected. The fusion method appears to be an effective compromise, significantly improving the SPP mode and approaching the accuracy of the RTK mode. It is important to note that these results are specific to streets with low-rise and open-sky buildings, and performance could vary in different environments. Also, on streets with high-rise buildings, the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 showed the best performance with the RTK mode, which offers high precision but may not always be available in all urban environments. The SPP mode is less reliable with larger error margins, indicating the challenges posed by urban navigation. The fusion method consistently improves the SPP mode and provides a more stable error margin, which could be due to the integration of additional sensors or data sources. The overall trends suggest that both devices are capable of similar performance, with the specific method used for positioning having a significant impact on the accuracy. The SPP mode remains the least reliable, and the fusion method appears to be a robust alternative that offers improved accuracy over the SPP mode and which is close to the RTK performance. The consistent improvement seen with the fusion method in both locations suggests that it may be a viable option for urban navigation where the RTK mode is not available. On the other hand, on Khorramshahr Street, the positioning performance trends for both the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 are similar to those observed on Daneshgah and Naderi Streets, with the RTK mode offering the highest precision. The SPP mode remains less reliable, while the fusion method consistently outperforms both the SPP and RTK methods, offering superior accuracy and robustness in challenging urban environments. These trends suggest that the fusion method could be a reliable approach for urban navigation when RTK signals are not optimally available. From this research, it can be concluded that selecting the appropriate GNSS method is crucial, considering the environment and correct signal availability. Multi-sensor fusion techniques significantly enhance the positioning accuracy in complex urban areas. In most cases, the RTK mode outperformed the SPP mode, offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The fusion method, combining GNSS data with INS data, offers a robust alternative, approaching RTK accuracy and, in some cases, obtaining better accuracy.