Their findings indicated that the average carrier-to-noise ratio (CNR) typically reached 25 dB-Hz, while pseudo-range noise levels ranged between 8 and 12 m. [PERSON] et al. [7] introduced an algorithm based on the time-differenced carrier phase (TDCP) filter, which achieved meter-level positioning accuracy. Their research particularly highlighted the significance of carrier-to-noise ratio (CNR) values in determining the quality of smartphone measurements. References [8; 9; 10; 11] concluded that the traditional elevation-weight method is not suitable for smartphones, while the CNR-weighted approach was deemed more suitable and effective. Precise point positioning (PPP) is one of the methods employed to attain high-precision positioning, and several research studies have been conducted on implementing PPP with smartphones [12; 13]. [PERSON] et al. [14] utilized Global Ionospheric Map (GIM) products to correct for ionospheric delays in measurements collected with the Nexus 9, thereby achieving sub-meter-level positioning accuracy through GPS single-frequency (SF) PPP. In 2018, Xiaomi 8 made its debut as the world's first smartphone capable of observing dual-frequency (DF) raw GNSS measurements [15]. Since then, researchers have embarked on comprehensive investigations into the positioning performance of dual-frequency GNSS smartphones. In studies [16; 17], it was revealed that incorporating L5/E5a measurements can significantly enhance positioning accuracy. [PERSON] et al. [18] conducted an assessment of code measurements, considering pseudo-range noise and multi-path errors. Their results indicated that GPS satellites tend to exhibit more multi-path errors when compared to Galileo satellites, and that L5/E5a signals demonstrate higher quality than the L1/E1 signals. [PERSON] et al. [19] conducted a study on dual-frequency (DF) ionosphere-free (IF) PPP in an open-sky environment, utilizing GNSS measurements from the Xiaomi 8 smartphone. Their results showed that DF IF PPP was capable of achieving sub-meter-level positioning accuracy. [PERSON] et al. [20] conducted an investigation into the DF PPP performance of the Xiaomi 8 smartphone in both static and dynamic scenarios. Their experimental results indicated that in static scenarios, the convergence accuracy could reach sub-meter levels, but it required a lengthy convergence time of up to 3 h. In dynamic scenarios, due to the limited number of available satellites, positioning errors reached several meters or even tens of meters. [PERSON] et al. [21] conducted an investigation into the precision limits of SF and DF PPP using smartphones in a static open-sky environment. Their experimental results indicated that the convergence accuracy showed slight variations among different smartphones, with sub-meter-level precision attainable. However, there were notable differences in convergence times observed among the various smartphones. [PERSON] et al. [22] demonstrated that multi-GNSS DF positioning performs better than SF positioning. In studies [23; 24; 25], researchers conducted an analysis of the quality of raw GNSS measurements and performed PPP experiments in both static and dynamic scenarios. Their results indicated that smartphones can achieve sub-meter positioning accuracy in static open-sky environments and meter-level positioning accuracy in dynamic scenarios. In addition to absolute positioning PPP methods, there is also research on relative positioning methods, with real-time kinematics (RTK) being a primary technique. There have also been studies focusing on RTK applications for smartphones [26; 27; 28]. In studies [29; 30], experiments were conducted to validate the performance of smartphone RTK, and the results demonstrated that it can achieve meter-level positioning accuracy. [PERSON] et al. [31] introduced a robust RTK scheme utilizing sliding window-based factor graph optimization, which achieved sub-meter-level positioning accuracy in open-sky urban areas and meter-level accuracy in complex urban environments. [PERSON] et al.