## 2 Related Work  
Numerous investigations have been undertaken to assess and augment the localization accuracy of smartphones. Nevertheless, it is important to highlight that a significant portion of these studies have primarily concentrated on scrutinizing performance in optimal open-sky conditions, thereby neglecting the inherent reality that real-world smartphone positioning activities are predominantly executed within the intricate and demanding contexts of urban environments, where formidable challenges and adverse conditions persist [21; 22; 23; 24; 25]. In [2], an approach was introduced to enhance the integration of GNSS/INS and visual inertial navigation systems (VINSs). This approach involved the application of the Semantic Proximity Update (SPU) technique, making use of a pre-trained model for real-time object detection. SPU entails the identification of geo-referenced objects along with tracking their relative movement to ascertain an absolute position. The empirical findings indicated that the proposed method yielded significantly reduced errors in horizontal and three-dimensional positioning, with respective decreases of 51.6% and 86.8% compared to a conventional integration technique. In [7], a TC multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system was designed to harness the complementary capabilities of these onboard technologies for urban navigation. In order to enhance navigation accuracy and computational efficiency, a novel LiDAR sliding-window plane-feature tracking approach was developed. The proposed integration of GNSS/INS/LiDAR demonstrated the ability to achieve submeter-level horizontal positioning accuracy in challenging GNSS environments, yielding substantial improvements of 73.3% in the east direction, 59.7% in the north direction, and 64.2% in the up direction (ENU) when compared to conventional GNSS/INS integration methods. In [12], two multi-constellation smartphone models were used. A The Samsung Galaxy S21+ (Samsung Electronics, Suwon, Republic of Korea) and the Xiaomi Mi 8 (Xiaomi Corporation, Beijing, China) were attached to a vehicle dashboard. The total root mean square error (RMSE) could be improved to less than 2 m by TC integrating PPP with the smartphone IMU in ideal conditions. In [26], digital surface models (DSMs) pertaining to diverse urban scenarios were simulated. In urban canyon environments, a considerable portion of epochs failed to provide a minimum of four visible satellites, and numerous epochs exhibited suboptimal position dilution of precision (PDOP). Simulations were conducted to explore various geometric configurations of 5G base stations surrounding the observer. An integration between 5G service and GNSS improved the redundancy and the quality of the solutions by using five 5G base stations around the observer. In [27], the LC algorithm was used with an iPhone 4 (Apple Inc., Cupertino, CA, USA) as a platform in a car dashboard, and the findings showed that under real GPS signal conditions, the RMSE could reach 2.07 m in the horizontal and 2.19 m in the vertical plane. This RMSE increased to 70.2 m horizontally and 16.5 m vertically within 30 s of GPS outage and decreased to 33.2 m horizontally and 15.7 m vertically after using non-holonomic constraints. In [28], with a Huawei Mate 8 (Huawei Technologies Co., Ltd., Shenzhen, China) smartphone inside a vehicle, two scenarios were examined the first using the smartphone IMU only, reaching 2000 m to the east and 100 to the north. Only 45 s of data and 10 s ofGPS outage were used during the test. Using LC integration between the phone sensors and the GPS, the error was decreased to 11 m, as mentioned during the outage. In [29], the performance of the Xiaomi Mi 8 smartphone, equipped with dual-band capabilities, in conjunction with a geodetic receiver was examined. Specifically, the study focused on investigating the influence of multipath effects on positioning accuracy.