## 1 Introduction  
Nowadays, real-time positioning services play an important role in several areas, such as public navigation [1]. The real-time-positioning information of users relies on the GNSS real-time satellite orbit, clock offset products on the server and the GNSS measurements of users [2]. The real-time products from the International GNSS Service (IGS) analysis centers are transmitted through the Networked Transport of RTCM via Internet Protocol (NTRIP). In addition, the real-time products from many IGS analysis centers are uploaded to Caster by the server, and users can access Caster to obtain real-time products through the Client [3]. The performance of GNSS real-time orbit and clock offset products has been greatly improved in recent years, and in terms of receiving GNSS measurements, users can use various types of receivers to receive real-time observations. Additionally, smartphones have become indispensable tools in daily life due to their high popularity. A low-cost GNSS chip was embedded in several smartphones, which can receive GNSS measurements [4]. In 2016, the operating system of Android N was released by Google; since then, an interface has been provided by Android smart devices, and the raw GNSSobservations could be exported [5]. Therefore, real-time positioning services based on the smartphone are possible. The study of GNSS smartphones can be divided into observation data quality evaluation and positioning performance analysis. Several studies have already researched the observation data quality assessment of smartphones. [PERSON] et al. evaluated the carrier-to-noise density ratio (C/N0) and pseudorange noise at GPS L1 frequency received by the Google/HTC Nexus 9 smartphone; the C/N0 was 10 dB-Hz lower than the geodetic-quality receiver, and the single-difference pseudorange residuals were between \(-20\) m and \(20\) m [6]. [PERSON] et al. compared the C/N0 and single-difference pseudorange residuals between short baseline stations and three smart devices, including the Samsung Samsung Galaxy S8, Huawei P10 and Google Nexus 9 tablet. The single-difference pseudorange was 3.09, 6-7 and 12-13 m for BDS, GPS and GLONASS, respectively [7]. [PERSON] et al. evaluated the C/N0 and pseudorange noise of the Huawei P20 smartphone, and the C/N0 was 9.4 dB-Hz lower than that of the Javad Alpha receiver, and the pseudorange noise was beyond 10 times higher than the receiver [8]. [PERSON] et al. found that the C/N0 were larger than 10 dB-Hz, and the percentages of the cycle slip rate for GPS, GLONASS and BDS are approximately 1.85%, 4.04% and 0.18%, respectively [9]. In terms of positioning using a smartphone, [PERSON] et al. used a Samsung Galaxy A7 smartphone to conduct BDS/GPS static and kinematic real-time kinematic (RTK) positioning; the static RTK positioning accuracy was better than 0.15 m and 0.25 m in horizontal and vertical directions, respectively, while kinematic RTK was better than 0.30 m and 0.45 m in horizontal and vertical directions, respectively [10]. [PERSON] et al. employed GPS L1 observations from a Xiaomi 8 mobile phone to perform PPP; the accuracies were 0.47 m, 0.91 m and 0.5 m in the east (E), north (N) and up (U) directions, respectively [11]. [PERSON] et al. showed that the positioning accuracy of single-frequency PPP could reach 0.5-0.6 m horizontally and 1.0-2.0 m vertically for Xiaomi 8 and Huawei Mate20/30 mobile phones [12]. [PERSON] et al. analyzed the GPS L1 signal from a Huawei P30 smartphone for differential code solution and were able to achieve positioning accuracies of 0.006, 0.005 and 0.015 m in the N, E and U directions, respectively, after one hour of continuous observation [13]. [PERSON] et al. established a smartphone multi-GNSS PPP model; the static PPP accuracy was 0.188, 0.165 and 0.761 m in the E, N and U directions, respectively, while the kinematic PPP was 0.928, 0.624 and 2.167 m [9].