# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 4 Conclusions and Discussion  
This paper analyzed the positioning performance of Mi 8 dual-frequency smartphone using a dual-frequency ionosphere-free combination PPP algorithm that considers the ISB and DCB. The results showed that the solutions of dual-frequency smartphone observations may achieve decimeter-level accuracy in static mode, which was comparable to the geodetic receiver in single-frequency mode,  
Figure 10: The number of satellites observed by smartphone. Figure 9: C/\(N_{0}\) of GPS, Galileo and GLONASS satellites (Receiver: red lines; Smartphone: black lines). but a long convergence time was required for PPP. In the kinematic mode, the data quality of the dual-frequency smartphone was poor, and continuous positioning results were only obtained with difficulty, having an offset of 4-5 m from the actual track. Compared with the geodetic receiver, the positioning accuracy of double-frequency smartphone was 3-5 m in kinematic mode, which was worse than the single-frequency data of the smartphone. In recent years, many studies have been proposed to deal with different types of observations using various algorithms. The positioning results of pseudorange observations are generally worse than those of carrier phase observations. Positioning accuracy within 5 m can be achieved using the pseudorange double-difference algorithm to process carrier phase smoothing of the single-frequency code observations [14]. The positioning results obtained by single-frequency carrier phase observations usually vary with the different algorithms. For the relative positioning method with single-frequency phase observations in a fast static mode, the positioning accuracy is similar to the PPP used in this paper, but the solution of the former was highly affected by the baseline length and the base station type (virtual station or physical station) [15]. In addition to that, the combination of pseudorange, carrier phase and doppler observations might reach a positioning accuracy of 0.6 m and 1.4 m in the horizontal and vertical directions, respectively, using the time series differential algorithm with the SNR-dependent weighting method [17]. As for the PPP, the results of single-frequency phase observation showed an internal accuracy of 0.37 m and 0.51 m for the horizontal and vertical components, which was different from the external accuracy obtained in this paper [16]. PPP of a single smartphone can acquire high-precision positioning results without external equipment, and the procedure of data collection is low-cost and convenient. However, the convergence time of the smartphone was much longer than that of the geodetic receiver, which could be solved by the introduction of other algorithms in static positioning in the future. In the process of PPP used in this paper, the unfixed integer ambiguity led to an extended time for convergence, and solving ambiguity may be helpful in speeding up the convergence [31; 32]. Smartphone hardware limitations, such as the crowded PCB (printed circuit board) space of the smartphone, the thermal noise generated during operation, and the linearly polarized antenna, are detrimental to the positioning results. The application of external antennas is one commonly used method to improve data quality and positioning accuracy [33]. In this paper, static and kinematic tests were carried out in low multipath environments such as with an open sky roof and in a playground. No experiment was conducted in a high multipath environment. The reason for this was that the smartphone cannot observe enough satellites due to its antenna, and it was difficult to obtain the positioning results after PPP processing. The PPP accuracy of the dual-frequency smartphone can meet the requirements of most application scenarios, and can be used in semi-professional fields such as smart city, Internet of things (IoT), and smart transportation, as well as in professional fields with low precision requirements such as map updating and cadastral survey [34]. Conceptualization, Q.W. and M.S. ; Data curation, C.Z. and P.Z.