## 2 Related Work
The results indicated that the Galileo E5 frequency exhibited a horizontal RMSE of 4.57 m, while the L1/E1 multi-constellation frequency demonstrated a 2D RMSE of 5.36 m.  
In [30], the authors evaluated the quality of contemporary Android smartphones' multi-GNSS observations Xiaomi MI 8, 9 (Xiaomi Corporation, Beijing, China), and Huawei P20, P30 (Huawei Technologies Co., Ltd., Shenzhen, China)). Signal strength, observational noise, and satellite tracking capabilities were all included in the study. The positioning experiment demonstrated that it was possible to solve a smartphone positioning problem precisely at the cm level even when there were fixed integer ambiguities. In [31], the analysis commenced with an examination of methodologies that solely relied on data acquired from vehicle on-board sensors. It demonstrated that while certain approaches can attain the requisite accuracy for autonomous driving, they encounter constraints imposed by the substantial cost of the sensors and performance limitations encountered in diverse driving scenarios, including but not limited to cornering, intersections, and challenging environmental conditions such as low light or snowy conditions. In view of this analysis, the prospect of developing cost-effective localization systems exhibiting robustness and high accuracy arose through the fusion of off-board information with sensory input. However, it is crucial to acknowledge that the efficacy of such systems is contingent upon factors such as the infrastructure availability or penetration rate of neighboring connected vehicles, as well as the level of network service. In [32], an algorithm named \"The Whole Work\" was introduced, which involved tightly coupling GNSS and IMU within a filter for robust GNSS positioning with local ground augmentation strategies. The Unscented Kalman Filter (UKF) was employed as a primary estimator. The recorded data were obtained from a Samsung S20 Ultra device, utilizing code observations and carrier-phase measurements. The application of this algorithm resulted in improvements in horizontal accuracy. Specifically, the d50 accuracy was enhanced by 6.4%, the d80 accuracy by 5.0%, and the d95 accuracy by 18.1% when using the combination of pseudorange and Doppler measurements. d50 represented the radius of the circle within which 50% of the horizontal position errors fell. A smaller d50 value indicated higher accuracy, meaning that more of the position estimates were closer to the true location. The author used d50, along with d80 and d95, to provide a comprehensive evaluation of the distribution of errors and better illustrate the performance of the algorithm across different probability levels. Furthermore, when integrating pseudorange, Doppler, and phase measurements, the d50 accuracy was improved by 8.6%, the d80 accuracy by 8.3%, and the d95 accuracy by 19.8%. In [33], a literature review highlighted the limitations of traditional GNSS/INS integration methods, such as the Extended Kalman Filter (EKF) and the Robust Kalman Filter (RKF), particularly in urban environments characterized by frequent signal obstructions and measurement noise. In addressing these challenges, the Factor Graph Optimization (FGO) method has shown superior robustness and accuracy. In high-precision inertial navigation experiments, the FGO approach yielded improvements of up to 81.1% in position, 69.8% in velocity, and 75.1% in attitude estimation compared to EKF and RKF. Even in consumer-grade inertial systems, the FGO model achieved up to 36.4% improvement in position, 73.8% in velocity, and 62.3% in attitude, demonstrating its effectiveness across varying sensor qualities. In [34], a comprehensive analysis of double-differenced GNSS observations from GPS, GLONASS, and Galileo systems revealed significant variability in noise characteristics across constellations and signal types.