## 1 Introduction  
The rapid development of smartphones and low-cost GNSS chipsets has greatly improved people's quality of life. At the Developer Conference in May 2016, Google announced that they would provide the Android Nought operating system with an interface to obtain GNSS raw observations, which is of epoch-making significance for smartphone positioning. In the early stages, few smartphones could collect high-quality GNSS raw observations. The Samsung Galaxy S7 was used to collect and analyze GNSS raw observations, and the results reflected that the quality of the smartphone antenna and its duty cycle would affect the observations [1]. [PERSON] et al. [2] and [PERSON] et al. [3] analyzed the GNSS raw observations of the nexus9 tablet, incorporating the Doppler and carrier observations into the calculation. They concluded that the Carrier-to-Noise ratio (C/N0) of GNSS raw observations of mobile devices is 10 dB-Hz lower than the mean of the geodesic receiver, and the horizontal and vertical root mean square (RMS) errors reached 0.8 m and 1.4 m. After the Google Developer Conference, various manufacturers had successively launched smartphones that supported obtaining GNSS raw observations. [PERSON] et al. [4] used the Huawei P10 smartphone to conduct the pseudorange point positioning test and the horizontal positioning accuracy could reach 10 m, but the positioning accuracy of elevation direction was poor. [PERSON] et al. [5] used the Samsung Galaxy smartphones to conduct maritime differential positioning tests. The results showed that the horizontal positioning accuracy could also reach 10 m, which satisfied most marine navigation accuracy requirements. Although results obtained in the above experiments are of great significance to the improvement of the positioning accuracy of smartphones, there is still a certain gap between the positioning accuracy of geodetic receivers [6; 7]. In May 2018, Xiaomi launched the first GNSS smartphone that supports (L1/E1+L5/E5) dual-frequency, which meant that smartphone positioning had entered a new stage [8]. Scholars began to study high-precision positioning algorithms based on smartphones [9; 10]. Researchers [11; 12] evaluated the GNSS raw observations of the Xiaomi 8 smartphone and found that the carrier was not affected by the duty cycle. Then, they conducted Real-Time Kinematic (RTK) and PPP tests and obtained horizontal RMS positioning errors of 1.17 m and 2.23 m. According to the phenomenon that the difference of the smartphone between the pseudorange observation value and the carrier is not fixed, [PERSON] et al. [13] proposed a precise single-point positioning method. It is based on the dual clock difference of the smartphone. The horizontal and elevation positioning accuracy can reach 0.81 m and 1.65 m.  
With the continuous improvement of smartphone performance, the quality of the GNSS raw observations of the smartphone has also improved. However, the ionospheric delay error still affects the positioning effect. Weakening the ionospheric delay is the key to improving the positioning accuracy of smartphones. The effect of Global Ionospheric Map (GIM) files to correct the ionospheric delay is significantly inferior to the post-GIM data [13], which is as difficult as the Klobuchar model to meet the real-time requirements of smartphone positioning. [PERSON] et al. [14] proposed an ionospheric model based on spherical cap harmonic analysis, which accurately reflects regional TEC changes and provides ionospheric prediction functions. [PERSON] et al. [15] used the Beidou and GPS dual systems to establish an ionospheric model in China. The results show that the dual systems can significantly improve the accuracy of the ionospheric model. [PERSON] et al. [16] uses the observations obtained by CORS to monitor the changes in the inversion of regional or global ionospheric distribution in real-time.