Accuracy analysis of BDS-3 PPP-B2b precise point positioning over Sri Lanka

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Achila, Prasanna H.M.I. Prasanna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6863905/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The BeiDou Navigation Satellite System (BDS-3) provides a real-time precise point positioning (PPP) solution with its BDS PPP-B2b signal, which does not rely on local base stations, and therefore has become a promising solution for GNSS-based surveying applications in real-time. The present study assesses the accuracy, convergence duration and environmental impact of PPP-B2b in the case of Sri Lanka in comparison with traditional post-processed GNSS approaches such as Differential Global Navigation Satellite System (DGNSS), Single Point Positioning (SPP), and PPP-Static. The study was carried out in three different sites Maharagama (open field), Thalangama (semi-urban, adjacent to a body of water) and Diyagama (dense urban area) to evaluate PPP-B2b’s performance on varying terrain-different environmental conditions. The results demonstrate that PPP-B2b allows sub-meter positioning with an average horizontal accuracy of 0.4 m and vertical accuracy of 2.3 m, and the open-field site (Maharagama) experiences the best accuracy due to geomorphological factors and complete satellite visibility, whereas Thalangama gave moderate deviations due to multipath generated from reflections on the water. Diyagama site had the least accuracy, because of signal obstruction, urban canyon and increasing the convergence time. Even though PPP-B2b is a low-cost and independent solution that does not require terrestrial infrastructure, it is limited by several factors that result in high convergence time (typically up to 10-15 minutes) and errors generated through multipath and ionospheric delay. These limitations notwithstanding, satellite-based PPP services such as PPP-B2b are still viable options for real-time positioning and atmospheric applications that do not require internet connectivity. Real-time PPP-B2b Precise point positioning Convergence time BDS-3 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction The modernization of Global Navigation Satellite Systems (GNSS) has revolutionized Positioning, Navigation, and Timing (PNT) services, delivering centimeter-level accuracy across various sectors such as transport, agriculture, disaster management, and infrastructure development (Yang et al., 2020a; Yu et al., 2022a). One significant advancement is the BeiDou Navigation Satellite System (BDS-3), which has opened avenues for real-time Precise Point Positioning (PPP) using the PPP-B2b signal (Kouba and Héroux, 2001). This service transmits real-time satellite orbit, clock, and differential code bias (DCB) corrections via geostationary satellites, achieving decimeter-level accuracy that is independent of terrestrial Internet infrastructures, which can be critical in remote and disaster-prone areas (Kouba and Héroux, 2001; Afifi and El-Rabbany, 2015). However, the early implementation of PPP-B2b was not without critical issues, such as delays in its transmission (Chen et al., 2021), limitations in regional coverage, and degradation of the signal in urban areas (multipath, etc.) (Li et al., 2023). Reliance on geostationary satellites caused further performance degradation at high latitudes (Chen et al., 2009), while limited hardware and inadequate. documentation hindered progress (Li et al., 2020). Early experiments employed Software-Defined Receivers (SDRs) to decode PPP-B2b signals and found that BDS satellites in the Asia-Pacific region offered centimeter-to-decimeter-level orbit corrections and meter-level clock corrections (Lu et al., 2020a). However, due to dependence on single-constellation data and complications in real-time implementation, these studies reported extended convergence time. To overcome these limitations, recent studies have considered integrating PPP-B2b with multi-GNSS To overcome these limitations, recent studies have considered the integration of PPP-B2b with multi-GNSS constellations (GPS, Galileo and GLONASS), which improve redundancy, satellite visibility and convergence times (J. Lu et al., 2020). Despite this progress, challenges like inter-system bias and lengthy initialization periods still remain (Nie et al., 2021a). Now, the researchers highlight the prospect of broadening the global applicability of PPP-B2b via Low Earth Orbit (LEO) satellite augmentation (Yang et al., 2022), improved ground-based monitoring networks, and optimization for low-cost receivers (Ren et al., 2021a). This is especially important for countries such as Sri Lanka that face the challenges of varied topography and susceptibility to natural calamities which require accurate real-time positioning data for disaster management, environmental monitoring, and infrastructure development planning. Regional discrepancies in correction retrieval rates of 88.76% in China versus 60.91% in Sri Lanka highlight inadequate delivery of service (Wang et al., 2024). PPP-B2b obtains static horizontal and vertical accuracies of 2.4 cm and 2.3 cm, respectively, under an average convergence time of 17.7 minutes (X. Wang et al., 2024), its performance is inferior to providing global coverage and convergence efficiency for the Galileo High Accuracy Service (HAS) (Liu et al., 2022, Ren et al., 2021b). To fill in these gaps, hybrid solutions that combine the PPP-B2b approach with other multi-GNSS data and machine learning-based error mitigation techniques have been proposed (J. Wang et al., 2024, Kan et al., 2024). Multiple studies on kinematic level PPP-B2b improvement results have shown a global improvement of 68.4% accuracy with regard to stand-alone (Wei et al., 2025) through integration with GPS & Galileo using Helmert transformation. Yet, extensive assessments of the performance of PPP-B2b in diverse settings like urban canyons or high-latitude regions are hardly available (Wei et al., 2025). This paper evaluates the real-time precision, convergence time, and reliability of PPP-B2b for GNSS positioning in Sri Lanka, compared to traditional post-processed methods such as DGNSS, SPP, and PPP-Static. Also explores the environmental variables that impact the performance of PPP-B2b. Section 2 describes the datasets and methods used in the study, including multi-GNSS integration methodologies and error correction models. Section 3 provides the empirical results regarding the accuracy, convergence time, and stability of PPP-B2b. Section 4 discusses implications for global scalability and region-specific applications, including a case study on geospatial challenges in Sri Lanka. 2 Methodology The goal of this methodology is to analyze the accuracy of the GNSS positioning comparing the static and real-time PPP-B2b methods. follow a structured workflow (for example, see Fig. 1 ) that goes from data acquisition to accuracy assessment. Observation starts from the coordinates, GNSS receivers were installed both static and for real-time PPP configurations. In the static setup, data is obtained in two ways: with and without correction files, which enables the evaluation of external corrections on positioning accuracy. The raw GNSS data is processed in-situ for real-time PPP-B2b by making use of satellite-based corrections. The data processing involves various approaches: Direct GNSS processing where a base station is involved (DGNSS), Positioning at a single point without corrections (SPP) and PPP-static processing using the correct orbit and clock corrections. A systematic comparison considering position accuracy deviations of these approaches is presented. After data processing, accuracy comparison is implemented to evaluate the performance of the method. The accuracy performance results help to understand the reliability of static and real-time PPP-B2b frameworks under different conditions to evaluate the feasibility of PPP-B2b so that geospatial positioning standards can be improved across Sri Lanka. 2.1 Study Area The study area comprises the three selected sites in Colombo, Sri Lanka, under consideration for GNSS signal conditions diversity are shown in Fig. 2 . Thalangama (52B40054), This semi-open site, which is in close proximity to Thalangama Lake is influenced by vegetation as well as residential buildings and reflections from the water surface causing moderate signal blockage and multipath errors. Maharagama (52B40055), a control site consisting of open agricultural land, with minimal signal obstructions, is a suitable location for benchmarking GNSS performance. Diyagama (52B40052) is a dense urban area that can be considered an urban canyon environment where serious signal blockage and multipath interference degraded GNSS accuracy. Collectively, these sites encompass GNSS performance for ideal, moderate and heavily obstructed conditions. 2.2 Data Collection & Processing In this study, Data was collected using a TOKNAV T10 Pro GNSS receiver for both static GNSS pivotal error estimation, as well as real-time PPP-B2b positioning. For both static observations, the receiver was tripod-mounted, and data were recorded for 60 min sessions at each location. To reduce the effect of low-angle satellites, data were recorded only under low-altitude coverage, with satellites covering 15 degrees of altitude. Based on the above parameters, data were recorded in RINEX 3.05 format, which included location information including location ID, date and time of creation, comment, and data collected. Data were processed in two ways to evaluate the effect of correction files: with correction files and without correction files. The TOKNAV T10Pro gnss Receiver( T10Pro GNSS Receiver_Real-Time Kinematic (RTK)_GNSS Receiver_Products_TOKNAV , n.d.) was used for real-time PPP-B2b positioning and to configure the receiver in PPP-B2b Rover mode. The correction data converged in a stabilization time of 10 to 15 minutes, and then 5-second observations were taken for each point. The receiver’s tilt feature simulated a variety of real-world uneven positioning scenarios, and average accuracy of 1-second interval coordinate recordings was evaluated. All post-processing was performed by RTKLib, an open-source software program that supports Single Point Positioning (SPP), Differential GNSS (DGNSS), and PPP-Static methods. To enhance accuracy, DGNSS data were processed through the integration of rover and base station observations with IGS SP3/CLK files. In contrast, PPP-Static processing depends on highly accurate orbit and clock corrections and uses the tropospheric model of Saastamoinen to reduce computational load. The DGNSS with correction files used broadcast ionospheric corrections, SP3, CLK and an optional ionosphere (. ion), differential code bias (. dcb) files from CORS, IGS, MGEX, and CDDIS. When no corrections were included, the positioning relied only on broadcast ephemeris and GNSS receiver data, which resulted in less accurate positioning. Likewise, SPP with fixes did the right thing but remained pretty imprecise too. Corrected Static PPP was central to the successful outcome; The approach used ionospheric-free linear combinations and PPP ambiguity resolution (PPP-AR) to improve accuracy, with precise orbit and clock corrections. The last part was to compare the accuracies for all methods with respect to DGNSS, SPP, PPP-Static, and PPP-B2b in real-time. This analysis mainly contributes to evaluating the suitability of PPP-B2b corrections for real-time high-precision positioning in different GNSS environments in Sri Lanka. 3 Results & Findings In this section, results and findings realized with the proposed method will be highlighted and discussed. The PPP-B2b method exhibited good positional accuracy in Northing and Easting coordinates and closely matched the GPS control points, demonstrated by low deviation in Figures 3, 4, and 5. With correction files, the PPP-B2b accuracy was much closer to DGNSS accuracy than it was SPP accuracy at the three stations. Some stations showed greater deviations for the PPP-Static compared to PPP-B2b, indicating PPP-Static has further room for improvement in processing. The DGNSS showed stable positioning accuracy with low variability and is thus a more robust option for real-time applications. On the other hand, the PPP-Static showed much higher offsets at certain locations and needs better corrections and adjustments made. The raw PPP-B2b method shows a lot of potential but needs further tuning for more accurate performance. The DGNSS is the most robust, while the PPP-Static method needs further improvement to consistent accuracy. It is advised that each of these positioning methods will benefit from further trials to illustrate their overall effectiveness in various applications. 3.1 Horizontal Positioning Accuracy As demonstrated in table 1, The DGNSS method with corrections exhibited the greatest accuracy with a Northing error of -0.01m and an Easting error of 0.083 m. The PPP-B2b option was nearly as accurate as DGNSS and achieved Northing and Easting errors of 0.142 m and 0.093, respectively with real-time corrections. The SPP option with corrections demonstrated moderate accuracy. The highest error relative to the method with corrections were in Northing while the lowest was in Easting for SPP results without corrections, with SPP Northing being 1.331 m and Easting being 0.108 m. The PPP-Static option with corrections displayed large errors, especially at the Thalawagama location (52B40054). PPP-Static without corrections had the worst results, as expected, since correction data is necessary. The PPP-B2b option was still an adequate method without corrections because of its real-time functionality and performed quite well relative to traditional methods. Table 1 Comparative Analysis of Horizontal Positioning Errors for Various GNSS Processing Methods Method Corrections Used Average Northing Error (m) Average Easting Error (m) DGNSS Included -0.01 0.083 DGNSS Not Included -0.065 -0.01 SPP Included 0.849 0.36 SPP Not Included 1.331 0.108 PPP-Static Included 1.075 -4.857 PPP-Static Not Included -2.606 -4.753 PPP-B2b Real-Time Only 0.142 0.093 3.2. Vertical Positioning Accuracy As demonstrated in Figure 7, the level of accuracy associated with ellipsoidal height classified by GNSS processing methods are presented in Table 2. The greatest accuracy for vertical positioning came from DGNSS with corrections, exhibiting the lowest average error at 3.988 m, while DGNSS without corrections had a comparatively better average error at 3.761 m. PPP-B2b using real-time corrections, did provide a level of consistency and overall was ranked second with an average error of 2.244 m. PPP-Static and SPP failed for various reasons but PPP-Static with corrections had the overall highest average error of 15.458 m, increasing slightly to 8.119 m, without corrections or attempts to position. Likewise, SPP with corrections returned considerably large vertical errors at an average error of 14.745 m, but still improved slightly to 13.914 m, without corrections. This demonstrates the necessity of correction data for vertical positioning. Additionally, it is significant that PPP-B2b outperformed both modes of DGNSS, SPP, and PPP-Static while using corrections or not when addressing vertical height. This demonstrates that real-time PPP-B2b can be relied upon for accurate vertical positioning. Table 2 Comparison of Average Ellipsoidal Height Error for Different Positioning Methods Method Corrections Used Average Ellipsoidal Hgt. Error(m) DGNSS Included 3.988 Not Included 3.761 SPP Included 14.745 Not Included 13.914 PPP-Static Included 15.458 Not Included 8.119 PPP-B2b Real-Time Only 2.244 3.3 Area Impact on Accuracy Table 3 & 4 indicates that among the selected sites, positioning accuracy was significantly affected by certain environmental factors of PPP-B2b. The urban site of Diyagama displayed the largest deviation of 0.354 m, primarily due to substantial multipath errors of GNSS signals that were obstructed by high-rise buildings. The semi-urban area of Thalangama, which has a nearby waterbody, reported a slightly higher deviation of 0.358 m. Conversely, the lowest deviation of 0.208 m was recorded at the Maharagama open field GNSS setup, supporting the theory that there are minimal obstructions with optimal satellite visibility and low multipath signal disturbance. Table 3 Summary of Station Wise Accuracy Variations Station Name Environmental Con. Challenges N Error (m) E Error (m) Diyagama (52B40052) Dense urban area Urban canyon effect, severe multipath errors 0.244 -2.075 Thalangama (52B40054) Semi-urban, near water body Multipath from water reflections, moderate obstruction 0.113 0.340 Maharagama (52B40055) Open agricultural field Minimal interference, clear sky visibility 0.069 0.196 Table 4 Summary of Station Wise Deviation from GPS(m) Station Name Corrections Used Diyagama (52B40052) 0.354 Thalangama (52B40054) 0.358 Maharagama (52B40055) 0.208 3.4 Feasibility for Sri Lanka’s Survey Industry The proposed PPP-B2b offers a promising alternative in this context, as it minimizes dependence on traditional base stations and post-processing. While its horizontal accuracy is <0.4 m, making it suitable for cadastral and topographic surveys, its vertical accuracy averages 2.244 m, limiting its use for geodetic applications. Challenges include signal interference in cities and dependence on internet connectivity (Table 5). Table 5 Advantages and Limitations of PPP-B2b for the Sri Lankan Survey Industry Aspect Corrections Used Average Ellipsoidal Hgt. Error(m) Horizontal Accuracy < 0.4m (real-time, no correction files) Less precise than post-processed GNSS Vertical Accuracy 2.244m (WGS 84) Not at geodetic-grade accuracy Base Station Requirement Not needed - Real-time Use Suitable Dependent on signal availability Cost Lower operational costs Requires high-quality receiver and internet access 4 Discussion This study assessed the effectiveness of PPP-B2b for real-time geospatial applications in Sri Lanka and compared the accuracy, reliability, and practicality of PPP-B2b against SPP, DGNSS, and PPP-Static. The environments included open-field, semi-urban, and dense urban sites, with analysis across both horizontal (Northing/Easting) and vertical (ellipsoidal height) accuracy. 4.1 Accuracy and Performance Comparison The PPP-B2b technique exhibited sub-meter horizontal accuracy of 0.2 to 3.6 meters and vertical accuracy of 2.244 meters in a real-time scenario, outperforming SPP and providing accuracy similar to that of DGNSS, without the need for local base stations. This independence from ground infrastructure makes PPP-B2b extremely suitable for remote or rural surveying applications. However, its vertical accuracy is inferior to that of post - processed PPP-Static, which can achieve centimeter-level precision for longer observation periods. For applications requiring millimeter-level accuracy, PPP-B2b is not suitable; however, for applications requiring immediate results, such as cadastral mapping, engineering surveys, hydrographic surveys, and infrastructure projects, it offers a reasonable compromise. The convergence time (10-15 minutes) of PPP-B2b is a significant drawback, making high-precision positioning slower compared to the near-instantaneous corrections of RTK. Nonetheless, during the convergence setup phase, the accuracy of RT-PPP is very close to RTK positioning. Moreover, errors from equatorial ionospheric disturbances and signal reflections due to tropical weather in Sri Lanka require correction models or multi-GNSS integration to rectify atmospheric delays. Advances in satellite clock/orbit corrections, AI-based error mitigation, and low-cost receivers in the future may help address some of these challenges and increase the applicability of PPP-B2b. 4.2 Environmental Impact on Performance Table 4 The findings indicate that environmental factors had a significant impact on PPP-B2b performance. • Open field sites (e.g. Maharagama) showed the best accuracy as no objects obstructed the satellite's view. • Semi-urban areas (e.g. Thalangama) resulted in moderate errors due to multipath effects and partial obstructions. • The most difficult locations were dense urban environments (e.g. Diyagama), where urban canyon effects and signal obstruction reduced positioning reliability. To overcome these shortcomings, hybrid solutions such as integrating PPP-B2b with satellite-based augmentation services (SBAS), including the EU's Galileo High Accuracy Service (HAS) or the Centimeter-Level Augmentation Service (CLAS) of Japan's Quasi-Zenith Satellite System (QZSS) and Indian GPS Aided GEO Augmented Navigation (GAGAN), can provide real-time corrections. The introduction of PPP-B3b will also enhance robustness in signal-depleted regions when integrating PPP-B2b with ground-based receiver-ranging technologies such as DGNSS or RTK. Additionally, the generation of localized PPP corrections using a Sri Lanka-specific Continuous Operational Reference Station (CORS) network will improve accuracy by accounting for atmospheric and geodynamic anomalies in and around the Sri Lankan region. 4.3 Future Prospects and Recommendations For wider adoption, a low-cost multi-frequency PPP-B2b receiver is essential to democratize access in Sri Lanka’s surveying industry, especially benefiting small-scale practitioners and rural projects. Emphasizing LEO satellite constellations (e.g., Starlink) is crucial. Furthermore, research into making PPP-B2b more future-proof through faster correction dissemination and increased availability of GNSS signals in obstructed environments is needed. These processes will bridge the gap between the current capabilities of PPP-B2b and the precision demands of various geospatial applications in Sri Lanka. Despite these challenges, PPP-B2b presents a low-cost, scalable alternative to classical GNSS approaches. By reducing operational costs and infrastructure barriers through detachment from local reference networks, precise positioning becomes more accessible for Sri Lanka’s surveying sector. Its real-time capability is critical for time sensitive applications such as disaster management and navigation, but signal stability in challenging terrains remains a concern. 5 Conclusion Based on this study, the authors believe that PPP-B2b is a suitable and low-cost RTK solution for real-time positioning of geospatial applications in Sri Lanka, capable of delivering centimeter to sub-meter accuracies without ground-based infrastructure. Its convergence time of 10 to 15 minutes makes it unsuitable for immediate high-precision tasks (e.g., legal surveys); PPP-B2b achieves stable, reliable accuracy within the region following convergence, which makes it powerful for large-scale or remote survey applications where millimeter-level precision is not critical. Multi-GNSS integration, hybrid systems (PPP-B2b with RTK or DGNSS at convergence), and Inertial Navigation Systems (INS) are tools that help mitigate environmental challenges like signal obstructions and ionospheric disturbances and improve stability. While PPP-B2b is not a substitute for RTK in ultra-high-precision use cases, the operational sensor flexibility offered by PPP-B2b, along with scalability and lower infrastructure costs, represents substantial advantages for the surveying, infrastructure development, and disaster management sectors in Sri Lanka. The continued development of multi-constellation support, convergence algorithms, and cost-effective receivers will cement its place in contemporary geospatial processes, providing a bridge between accessibility and accuracy in resource-constrained scenarios. Declarations Author Contribution A.D.G.R. conducted the research, performed the data collection and analysis, and wrote the main manuscript text. P.H.M.I. the research, contributed to the methodology, and reviewed and edited the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgement I extend my sincere gratitude to my research supervisor, Prof. H.M.I. Prasanna, for his invaluable guidance, insightful advice, and unwavering support throughout this research journey. His expertise and encouragement have been instrumental in shaping the direction and success of this study. I express my heartfelt gratitude to Mr. Nissanka de Silva, Managing Director, Global GIS Pvt. Ltd. His support in providing essential equipment, data and other critical resources played a crucial role in conducting this study. I am also grateful to his team for their dedication and seamless coordination during the data collection process. Their efforts in ensuring proper fieldwork and logistical support were instrumental in maintaining the accuracy and reliability of this research. I also extend my appreciation to all the faculty members of the Department of Surveying and Geodesy, whose guidance and constructive feedback have enriched my academic experience. Additionally, I am grateful to my colleagues and friends for their collaboration, motivation, and encouragement throughout this research process. Finally, I must express my deepest appreciation to my family, whose unwavering support and love have sustained me not only during this research but throughout my life. Their encouragement has been my constant source of strength. Thank you to everyone who contributed to this effort, whether directly or indirectly. Your support has been indispensable in the completion of this thesis. References Afifi, A., El-Rabbany, A., 2015. Performance analysis of several GPS/Galileo precise point positioning models. Sensors (Switzerland) 15, 14701–14726. https://doi.org/10.3390/s150614701 Chen, H.C., Huang, Y.S., Chiang, K.W., Yang, M., Rau, R.J., 2009. The performance comparison between GPs and BeiDou-2/compass: A perspective from Asia. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch’eng Hsuch K’an 32, 679–689. https://doi.org/10.1080/02533839.2009.9671550 Chen, J., Zhao, X., Liu, C., Zhu, S., Liu, Z., Yue, D., 2021. Evaluating the Latest Performance of Precise Point Positioning in Multi-GNSS/RNSS: GPS, GLONASS, BDS, Galileo and QZSS. Journal of Navigation 74, 247–267. https://doi.org/10.1017/S0373463320000508 Kan, H., Hu, Z., Chen, G., Liu, X., Liu, C., Zhao, Q., 2024. Performance comparison of orbit and clock augmentation corrections from PPP-B2b, HAS and CLAS. Advances in Space Research 74, 668–681. https://doi.org/10.1016/j.asr.2024.04.029 Kouba, J., Héroux, P., 2001. Precise Point Positioning Using IGS Orbit and Clock Products. GPS Solutions 5, 12–28. https://doi.org/10.1007/PL00012883 Li, R., Zheng, S., Wang, E., Chen, J., Feng, S., Wang, D., Dai, L., 2020. Advances in BeiDou Navigation Satellite System (BDS) and satellite navigation augmentation technologies. Satellite Navigation. https://doi.org/10.1186/s43020-020-00010-2 Li, R., Zheng, S., Wang, E., Chen, J., Feng, S., Wang, D., Dai, L., 2020. Advances in BeiDou Navigation Satellite System (BDS) and satellite navigation augmentation technologies. Satellite Navigation. https://doi.org/10.1186/s43020-020-00010-2 Li, X., Barriot, J.P., Lou, Y., Zhang, W., Li, P., Shi, C., 2023. Towards Millimeter-Level Accuracy in GNSS-Based Space Geodesy: A Review of Error Budget for GNSS Precise Point Positioning. Surv Geophys. https://doi.org/10.1007/s10712-023-09785-w Liu, Y., Yang, C., Zhang, M., 2022. Comprehensive Analyses of PPP‐B2b Performance in China and Surrounding Areas. Remote Sens (Basel) 14. https://doi.org/10.3390/rs14030643 Lu, J., Guo, X., Su, C., 2020. Global capabilities of BeiDou Navigation Satellite System. Satellite Navigation 1. https://doi.org/10.1186/s43020-020-00025-9 Lu, X., Chen, L., Shen, N., 2020a. Decoding PPP Corrections from BDS B2b Signals Using a Software-defined Receiver: an Initial Performance Evaluation, IEEE Sensors Journal. In Press. Nie, Z., Xu, X., Wang, Z., Du, J., 2021a. Initial assessment of BDS PPP-B2b service: Precision of orbit and clock corrections, and PPP performance. Remote Sens (Basel) 13. https://doi.org/10.3390/rs13112050 Ren, Z., Gong, H., Peng, J., Tang, C., Huang, X., Sun, G., 2021a. Performance assessment of real-time precise point positioning using BDS PPP-B2b service signal. Advances in Space Research 68, 3242–3254. https://doi.org/10.1016/j.asr.2021.06.006 T10Pro GNSS Receiver_Real-time kinematic (RTK)_GNSS Receiver_Products_TOKNAV. (n.d.). Retrieved June 2, 2025, from https://www.toknav.cn/index.php?c=show&id=39 Wang, J., Shi, C., Zheng, F., Yang, C., Liu, X., Liu, S., Xia, M., Jing, G., Li, T., Chen, W., Li, Q., Hu, Y., Tian, Y., Shan, Y., 2024. Multi-frequency smartphone positioning performance evaluation: insights into A-GNSS PPP-B2b services and beyond. Satellite Navigation 5. https://doi.org/10.1186/s43020-024-00146-5 Wang, X., Zhou, K., Zhang, J., Li, H., Liang, H., Tu, M., Chen, Y., Qiu, C., Li, L., 2024. An investigation of PPP-B2b coverage and its performance in ZTD estimation and positioning in different regions. Survey Review. https://doi.org/10.1080/00396265.2024.2401665 Wei, H., Xiao, G., Zhou, P., Li, P., Xiao, Z., Zhang, B., 2025. Combining Galileo HAS and Beidou PPP-B2b with Helmert coordinate transformation method. GPS Solutions 29. https://doi.org/10.1007/s10291-024-01789-2 Yang, H., Ren, X., Liu, M., Zhang, X., 2024. Dual-frequency to five-frequency real-time precise point positioning using new BDS-3 PPP-B2b service. Earth, Planets and Space 76. https://doi.org/10.1186/s40623-024-02031-6 Yang, Y., Mao, Y., Sun, B., 2020a. Basic performance and future developments of BeiDou global navigation satellite system. Satellite Navigation 1. https://doi.org/10.1186/s43020-019-0006-0 Yu, Z., Pan, G., Zuo, X., Tang, Y., 2022a. Performance Analysis of Real-Time Precise Point Positioning Based on PPP-B2b Signal, in: Lecture Notes in Electrical Engineering. Springer Science and Business Media Deutschland Additional Declarations No competing interests reported. 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Achila","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDADA2bmBoYPFTZAJmPjASK1MDYwzjiTBtLSQKQWoEpm3rbDYA5eLebtZwwffNxhJ2/Oztj4mefMebu17YeBttTYROPSInMmx9hw5plkw53NjM2ScypuJ287kwjUciwttwGHFgmGtDRp3jbmBIPDjA0Sb87cTjY7ANTC2HAYtxb+Z+m//7bVg7Q0/+BtO5dsdv4hAS0SyceYGdsOg7S0SfK2HbAzu0HIFonHhyV7244bbgBqsZxxJjnB7AbQlgR8fuFPbPzws61a3uD84cM3PlTY2ZudT3/44EONDU4tGCARrDKBWOUgYE+K4lEwCkbBKBgZAACbqmb4jc6+cgAAAABJRU5ErkJggg==","orcid":"","institution":"Sabaragamuwa University","correspondingAuthor":true,"prefix":"","firstName":"Achila","middleName":"D.G.R.","lastName":"Achila","suffix":""},{"id":472630075,"identity":"dc4dca1c-b50a-4775-8af5-bac2dad2cbcd","order_by":1,"name":"Prasanna H.M.I. Prasanna","email":"","orcid":"","institution":"Sabaragamuwa University","correspondingAuthor":false,"prefix":"","firstName":"Prasanna","middleName":"H.M.I.","lastName":"Prasanna","suffix":""}],"badges":[],"createdAt":"2025-06-10 14:08:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6863905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6863905/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85070337,"identity":"658384a4-f793-46ca-b236-1ac7f5a03004","added_by":"auto","created_at":"2025-06-20 15:35:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37025,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology flow chart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/31aa3d2a792b803c598c1185.png"},{"id":85071873,"identity":"9d463c5d-17f5-4511-8eeb-e132d1b2c8a6","added_by":"auto","created_at":"2025-06-20 15:43:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":874345,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/761bf7c15e4d6ee28739162b.png"},{"id":85070340,"identity":"5f7d3a52-37c1-40e5-83a4-f7bac82401c0","added_by":"auto","created_at":"2025-06-20 15:35:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89871,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Northing \u0026amp; Easing Coordinates of 52B40052\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/5ef477b2681b969c64173a72.png"},{"id":85071879,"identity":"dafc0187-3608-47e5-8d75-3708a1be5dd1","added_by":"auto","created_at":"2025-06-20 15:44:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92079,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Northing \u0026amp; Easing Coordinates of 52B40054\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/a217fa6dd42848047fe794f2.png"},{"id":85070353,"identity":"7a7a57be-e6b2-4536-ba98-d2654e52b7cf","added_by":"auto","created_at":"2025-06-20 15:35:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84685,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Northing \u0026amp; Easing Coordinates of 52B40055\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/8825fe9c44cd762e3bc543b5.png"},{"id":85071877,"identity":"25a3fd44-d5ef-4205-9f6d-1c3e4d983a84","added_by":"auto","created_at":"2025-06-20 15:43:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117992,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Horizontal Positioning Errors for Various GNSS processing Methods\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/f360a82e61ca7948ae0899f8.png"},{"id":85070359,"identity":"2cd55943-b035-47b2-be78-ff1a5355c6ff","added_by":"auto","created_at":"2025-06-20 15:36:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67860,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Ellipsoidal Height Error Comparison Across GNSS Processing Methods\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/73be2630279ca106b6fe02a8.png"},{"id":92746705,"identity":"9417ea68-4d40-45e5-a01b-968881072bff","added_by":"auto","created_at":"2025-10-03 19:31:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1955777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863905/v1/1c2178be-e450-4d75-bae5-0d53b034508a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy analysis of BDS-3 PPP-B2b precise point positioning over Sri Lanka","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe modernization of Global Navigation Satellite Systems (GNSS) has revolutionized Positioning, Navigation, and Timing (PNT) services, delivering centimeter-level accuracy across various sectors such as transport, agriculture, disaster management,\u0026ensp;and infrastructure development (Yang et al., 2020a; Yu et al., 2022a). One significant advancement is the BeiDou\u0026ensp;Navigation Satellite System (BDS-3), which has opened avenues for real-time Precise Point Positioning (PPP) using the PPP-B2b signal (Kouba and H\u0026eacute;roux, 2001). This service transmits real-time satellite orbit, clock, and differential code bias (DCB) corrections via geostationary satellites, achieving decimeter-level accuracy that is independent of terrestrial Internet infrastructures, which can be critical in remote and disaster-prone areas (Kouba and H\u0026eacute;roux, 2001; Afifi and El-Rabbany, 2015).\u003c/p\u003e \u003cp\u003eHowever, the early implementation of PPP-B2b was not without\u0026ensp;critical issues, such as delays in its transmission (Chen et al., 2021), limitations in regional coverage, and degradation of the signal\u0026ensp;in urban areas (multipath, etc.) (Li et al., 2023). Reliance on geostationary\u0026ensp;satellites caused further performance degradation at high latitudes (Chen et al., 2009), while limited hardware\u0026ensp;and inadequate. documentation hindered progress (Li et al., 2020). Early experiments employed Software-Defined Receivers (SDRs) to decode PPP-B2b signals and found that BDS satellites in the Asia-Pacific region offered centimeter-to-decimeter-level orbit corrections\u0026ensp;and meter-level clock corrections (Lu et al., 2020a). However, due to dependence on single-constellation data and complications\u0026ensp;in real-time implementation, these studies reported extended convergence time.\u003c/p\u003e \u003cp\u003eTo overcome these limitations, recent studies have considered integrating PPP-B2b\u0026ensp;with multi-GNSS To overcome these limitations, recent studies have considered the integration of PPP-B2b\u0026ensp;with multi-GNSS constellations (GPS, Galileo and GLONASS), which improve redundancy, satellite visibility and convergence times (J. Lu et al., 2020). Despite this progress, challenges like inter-system bias\u0026ensp;and lengthy initialization periods still remain (Nie et al., 2021a). Now, the researchers highlight the prospect of broadening the global applicability of PPP-B2b via\u0026ensp;Low Earth Orbit (LEO) satellite augmentation (Yang et al., 2022), improved ground-based monitoring networks, and optimization for low-cost receivers (Ren et al., 2021a). This is especially important for countries such\u0026ensp;as Sri Lanka that face the challenges of varied topography and susceptibility to natural calamities which require accurate real-time positioning data for disaster management, environmental monitoring, and infrastructure development planning. Regional discrepancies in correction retrieval rates of 88.76% in\u0026ensp;China versus 60.91% in Sri Lanka highlight inadequate delivery of service (Wang et al., 2024).\u003c/p\u003e \u003cp\u003ePPP-B2b\u0026ensp;obtains static horizontal and vertical accuracies of 2.4 cm and 2.3 cm, respectively, under an average convergence time of 17.7 minutes (X. Wang et al., 2024), its performance\u0026ensp;is inferior to providing global coverage and convergence efficiency for the Galileo High Accuracy Service (HAS) (Liu et al., 2022, Ren et al., 2021b). To fill in these gaps, hybrid solutions that combine the\u0026ensp;PPP-B2b approach with other multi-GNSS data and machine learning-based error mitigation techniques have been proposed (J. Wang et al., 2024, Kan et al., 2024). Multiple studies on kinematic level PPP-B2b improvement results have shown a global improvement of\u0026ensp;68.4% accuracy with regard to stand-alone (Wei et al., 2025) through integration with GPS \u0026amp; Galileo using Helmert transformation. Yet, extensive assessments of the performance of PPP-B2b in diverse settings like urban\u0026ensp;canyons or high-latitude regions are hardly available (Wei et al., 2025).\u003c/p\u003e \u003cp\u003eThis paper evaluates the real-time precision, convergence time, and reliability of PPP-B2b for GNSS positioning in Sri Lanka, compared to traditional post-processed methods such as DGNSS, SPP, and PPP-Static. Also explores the environmental variables that impact the performance of PPP-B2b. Section 2 describes the datasets and methods used in the study, including multi-GNSS integration methodologies and error correction\u0026ensp;models. Section 3 provides the empirical results regarding the accuracy, convergence time, and stability of PPP-B2b. Section 4 discusses implications for global scalability and region-specific applications, including a case study on\u0026ensp;geospatial challenges in Sri Lanka.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe goal of this methodology is to analyze the accuracy of the GNSS positioning comparing\u0026ensp;the static and real-time PPP-B2b methods. follow\u0026ensp;a structured workflow (for example, see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) that goes from data acquisition to accuracy assessment.\u003c/p\u003e\n \u003cp\u003eObservation starts from the\u0026ensp;coordinates, GNSS receivers were installed both static and for real-time PPP configurations. In the static setup, data is obtained in two ways:\u0026ensp;with and without correction files, which enables the evaluation of external corrections on positioning accuracy. The raw GNSS data is processed in-situ for\u0026ensp;real-time PPP-B2b by making use of satellite-based corrections.\u003c/p\u003e\n \u003cp\u003eThe data\u0026ensp;processing involves various approaches: Direct GNSS processing where a base station is involved (DGNSS), Positioning at a single point without corrections (SPP) and PPP-static processing using the correct orbit and clock corrections. A systematic comparison considering position accuracy deviations of these approaches is\u0026ensp;presented.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAfter data processing, accuracy comparison is implemented to evaluate the performance of the method. The accuracy performance results help to understand the reliability of static and real-time PPP-B2b frameworks under different conditions to evaluate the feasibility of PPP-B2b so that geospatial positioning standards can be improved across Sri Lanka.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe study area comprises the three selected sites in Colombo,\u0026ensp;Sri Lanka, under consideration for GNSS signal conditions diversity are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Thalangama (52B40054), This semi-open site, which is in close proximity to Thalangama Lake is influenced by vegetation as well as residential buildings and reflections from the\u0026ensp;water surface causing moderate signal blockage and multipath errors. Maharagama (52B40055), a control site consisting of open agricultural land, with minimal\u0026ensp;signal obstructions, is a suitable location for benchmarking GNSS performance. Diyagama (52B40052) is a dense urban area that can be considered an urban canyon environment where serious signal\u0026ensp;blockage and multipath interference degraded GNSS accuracy. Collectively, these sites encompass GNSS performance for ideal, moderate\u0026ensp;and heavily obstructed conditions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Collection \u0026amp; Processing\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this study, Data was collected\u0026ensp;using a TOKNAV T10 Pro GNSS receiver for both static GNSS pivotal error estimation, as well as real-time PPP-B2b positioning. For both static observations, the receiver was tripod-mounted, and data were recorded for 60 min sessions at each location. To reduce the effect of low-angle satellites, data were recorded only under low-altitude coverage, with satellites covering 15 degrees of altitude. Based on the above parameters, data were recorded in RINEX 3.05 format, which included location information including location ID, date and time of creation, comment, and data collected. Data were processed in two ways to evaluate the effect of correction files: with correction files and without correction files.\u003c/p\u003e\n \u003cp\u003eThe TOKNAV T10Pro gnss Receiver(\u003cem\u003eT10Pro GNSS Receiver_Real-Time Kinematic (RTK)_GNSS Receiver_Products_TOKNAV\u003c/em\u003e, n.d.) was used for real-time PPP-B2b positioning and to\u0026ensp;configure the receiver in PPP-B2b Rover mode. The correction data converged in a stabilization time of 10 to 15 minutes, and then 5-second observations were taken\u0026ensp;for each point. The receiver\u0026rsquo;s tilt feature simulated\u0026ensp;a variety of real-world uneven positioning scenarios, and average accuracy of 1-second interval coordinate recordings was evaluated.\u003c/p\u003e\n \u003cp\u003eAll post-processing was performed by RTKLib, an open-source software program that supports Single Point Positioning\u0026ensp;(SPP), Differential GNSS (DGNSS), and PPP-Static methods. To enhance accuracy, DGNSS data were processed through the integration of rover and base station observations\u0026ensp;with IGS SP3/CLK files. In contrast, PPP-Static processing depends on highly accurate orbit\u0026ensp;and clock corrections and uses the tropospheric model of Saastamoinen to reduce computational load.\u003c/p\u003e\n \u003cp\u003eThe DGNSS with correction files\u0026ensp;used broadcast ionospheric corrections, SP3, CLK and an optional ionosphere (. ion),\u0026ensp;differential code bias (. dcb) files from CORS, IGS, MGEX,\u0026ensp;and CDDIS. When no corrections were included, the positioning relied only on broadcast ephemeris\u0026ensp;and GNSS receiver data, which resulted in less accurate positioning. Likewise, SPP with fixes did the right thing but remained pretty\u0026ensp;imprecise too. Corrected Static PPP was central\u0026ensp;to the successful outcome; The approach used ionospheric-free linear combinations and PPP ambiguity resolution (PPP-AR) to improve accuracy,\u0026ensp;with precise orbit and clock corrections.\u003c/p\u003e\n \u003cp\u003eThe last part was to compare the accuracies for all methods with respect to DGNSS, SPP, PPP-Static, and\u0026ensp;PPP-B2b in real-time. This analysis mainly contributes to evaluating the suitability of PPP-B2b corrections for real-time high-precision positioning in different GNSS environments in Sri Lanka.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results \u0026 Findings","content":"\u003cp\u003eIn this section, results and findings realized with the proposed method will be highlighted and discussed.\u003c/p\u003e\n\u003cp\u003eThe PPP-B2b method exhibited good positional accuracy in Northing and Easting coordinates and closely matched the GPS control points, demonstrated by low deviation in Figures 3, 4, and 5. With correction files, the PPP-B2b accuracy was much closer to DGNSS accuracy than it was SPP accuracy at the three stations. Some stations showed greater deviations for the PPP-Static compared to PPP-B2b, indicating PPP-Static has further room for improvement in processing. \u0026nbsp;The DGNSS showed stable positioning accuracy with low variability and is thus a more robust option for real-time applications. On the other hand, the PPP-Static showed much higher offsets at certain locations and needs better corrections and adjustments made. The raw PPP-B2b method shows a lot of potential but needs further tuning for more accurate performance. The DGNSS is the most robust, while the PPP-Static method needs further improvement to consistent accuracy. It is advised that each of these positioning methods will benefit from further trials to illustrate their overall \u0026nbsp; \u0026nbsp; \u0026nbsp;effectiveness in various applications.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.1 Horizontal Positioning Accuracy\u003c/h2\u003e\n\u003cp\u003eAs demonstrated in table 1, The DGNSS method with corrections exhibited the greatest accuracy with a Northing error of -0.01m and an Easting error of 0.083 m. The PPP-B2b option was nearly as accurate as DGNSS and achieved Northing and Easting errors of 0.142 m and 0.093, respectively with real-time corrections. The SPP option with corrections demonstrated moderate accuracy. The highest error relative to the method with corrections were in Northing while the lowest was in Easting for SPP results without corrections, with SPP Northing being 1.331 m and Easting being 0.108 m. The PPP-Static option with corrections displayed large errors, especially at the Thalawagama location (52B40054). PPP-Static without corrections had the worst results, as expected, since correction data is necessary. The PPP-B2b option was still an adequate method without corrections because of its real-time functionality and performed quite well relative to traditional methods. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eComparative Analysis of Horizontal Positioning Errors for Various GNSS Processing Methods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eCorrections Used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003eAverage Northing Error (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003eAverage Easting Error (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003eDGNSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eIncluded\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003eDGNSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003eSPP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eIncluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003eSPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003ePPP-Static\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eIncluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e-4.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003ePPP-Static\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e-2.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e-4.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2416%;\"\u003e\n \u003cp\u003ePPP-B2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4159%;\"\u003e\n \u003cp\u003eReal-Time Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7951%;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5474%;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.2. Vertical Positioning Accuracy\u003c/h2\u003e\n\u003cp\u003eAs demonstrated in Figure 7, the level of accuracy associated with ellipsoidal height classified by GNSS processing methods are presented in Table 2. The greatest accuracy for vertical positioning came from DGNSS with corrections, exhibiting the lowest average error at 3.988 m, while DGNSS without corrections had a comparatively better average error at 3.761 m. PPP-B2b using real-time corrections, did provide a level of consistency and overall was ranked second with an average error of 2.244 m. PPP-Static and SPP failed for various reasons but PPP-Static with corrections had the overall highest average error of 15.458 m, increasing slightly to 8.119 m, without corrections or attempts to position. Likewise, SPP with corrections returned considerably large vertical errors at an average error of 14.745 m, but still improved slightly to 13.914 m, without corrections. This demonstrates the necessity of correction data for vertical positioning. Additionally, it is significant that PPP-B2b outperformed both modes of DGNSS, SPP, and PPP-Static while using corrections or not when addressing vertical height. This demonstrates that real-time PPP-B2b can be relied upon for accurate vertical positioning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of Average Ellipsoidal Height Error for Different Positioning Methods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eCorrections Used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eAverage Ellipsoidal Hgt. Error(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eDGNSS\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eIncluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eSPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eIncluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e13.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePPP-Static\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eIncluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eNot Included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePPP-B2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eReal-Time Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e\n\u003ch2\u003e3.3 Area Impact on Accuracy\u003c/h2\u003e\n\u003cp\u003eTable\u0026ensp;3 \u0026amp; 4 indicates that among the selected sites, positioning accuracy was significantly affected by certain environmental factors of PPP-B2b. The urban site of Diyagama displayed the largest deviation of 0.354 m, primarily due to substantial multipath errors of GNSS signals that were obstructed by high-rise buildings. The semi-urban area of Thalangama, which has a nearby waterbody, reported a slightly higher deviation of 0.358 m. Conversely, the lowest deviation\u0026ensp;of 0.208 m was recorded at the Maharagama open field GNSS setup, supporting the theory that there are minimal obstructions with optimal satellite visibility and low multipath signal disturbance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSummary of Station Wise Accuracy Variations\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"418\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eStation Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003cp\u003eCon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eChallenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003cp\u003eError (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Error (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDiyagama (52B40052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDense urban area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eUrban canyon effect, severe multipath errors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-2.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eThalangama (52B40054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSemi-urban, near water body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMultipath from water reflections, moderate obstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eMaharagama (52B40055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eOpen agricultural field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal interference, clear sky visibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eSummary of Station Wise Deviation from GPS(m)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eStation Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCorrections Used\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eDiyagama (52B40052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eThalangama (52B40054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMaharagama (52B40055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.4 Feasibility for Sri Lanka\u0026rsquo;s Survey Industry\u003c/h2\u003e\n\u003cp\u003eThe proposed PPP-B2b offers a promising alternative in this context, as it minimizes dependence on traditional base stations and post-processing. While its horizontal accuracy is \u0026lt;0.4 m, making it suitable for cadastral and topographic surveys, its vertical accuracy averages 2.244 m, limiting its use for geodetic applications. Challenges include signal interference in cities and dependence on internet connectivity (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eAdvantages and Limitations of PPP-B2b for the Sri Lankan Survey Industry\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCorrections Used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAverage Ellipsoidal Hgt. Error(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eHorizontal Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026lt; 0.4m (real-time, no correction files)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLess precise than post-processed GNSS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eVertical Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e2.244m (WGS 84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNot at geodetic-grade accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eBase Station Requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNot needed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eReal-time Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDependent on signal availability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLower operational costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRequires high-quality receiver and internet access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study assessed the effectiveness of PPP-B2b for real-time geospatial applications in Sri Lanka and compared\u0026ensp;the accuracy, reliability, and practicality of PPP-B2b against SPP, DGNSS, and PPP-Static. The environments included open-field, semi-urban, and dense urban sites, with analysis across both horizontal (Northing/Easting) and vertical (ellipsoidal height) accuracy.\u003c/p\u003e\n\u003ch2\u003e4.1 Accuracy and Performance Comparison\u003c/h2\u003e\n\u003cp\u003eThe PPP-B2b technique exhibited sub-meter horizontal accuracy of 0.2 to 3.6 meters and vertical accuracy of 2.244 meters in a real-time scenario, outperforming SPP and providing accuracy similar to that of DGNSS, without\u0026ensp;the need for local base stations. This independence from ground infrastructure makes PPP-B2b extremely\u0026ensp;suitable for remote or rural surveying applications. However, its vertical accuracy is inferior\u0026ensp;to that of post - processed PPP-Static, which can achieve centimeter-level precision for longer observation periods. For applications requiring millimeter-level accuracy, PPP-B2b is not suitable; however, for applications requiring immediate results,\u0026ensp;such as cadastral mapping, engineering surveys, hydrographic surveys, and infrastructure projects, it offers a reasonable compromise.\u003c/p\u003e\n\u003cp\u003eThe convergence time (10-15 minutes)\u0026ensp;of PPP-B2b is a significant drawback, making high-precision positioning slower compared to the near-instantaneous corrections of RTK. Nonetheless, during the convergence setup phase, the accuracy of\u0026ensp;RT-PPP is very close to RTK positioning. Moreover, errors from equatorial ionospheric disturbances and signal\u0026ensp;reflections due to tropical weather in Sri Lanka require correction models or multi-GNSS integration to rectify atmospheric delays. Advances\u0026ensp;in satellite clock/orbit corrections, AI-based error mitigation, and low-cost receivers in the future may help address some of these challenges and increase the applicability of PPP-B2b.\u003c/p\u003e\n\u003ch2\u003e4.2 Environmental Impact on Performance\u003c/h2\u003e\n\u003cp\u003eTable\u0026ensp;4 The findings indicate that environmental factors had a significant impact on PPP-B2b performance.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp;Open field sites (e.g. Maharagama) showed the best accuracy as no objects obstructed the satellite\u0026apos;s view.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp;Semi-urban areas (e.g. Thalangama) resulted in moderate errors due to multipath effects and partial obstructions.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp;The most difficult locations were dense urban environments (e.g. Diyagama), where urban canyon effects and signal obstruction reduced positioning reliability.\u003c/p\u003e\n\u003cp\u003eTo overcome these shortcomings, hybrid solutions such as integrating PPP-B2b with satellite-based augmentation services (SBAS), including the EU\u0026apos;s Galileo High Accuracy Service (HAS) or the Centimeter-Level Augmentation Service (CLAS) of Japan\u0026apos;s Quasi-Zenith Satellite System (QZSS) and Indian GPS Aided GEO Augmented Navigation (GAGAN), can provide real-time corrections. The introduction of PPP-B3b will also enhance robustness in signal-depleted regions when integrating PPP-B2b with ground-based receiver-ranging technologies such as DGNSS or RTK. Additionally, the generation of localized PPP corrections using a Sri Lanka-specific Continuous Operational Reference Station (CORS) network will improve accuracy by accounting for atmospheric and geodynamic anomalies in and around the Sri Lankan region.\u003c/p\u003e\n\u003ch2\u003e4.3 Future Prospects and Recommendations\u003c/h2\u003e\n\u003cp\u003eFor wider adoption, a low-cost multi-frequency PPP-B2b receiver is essential to democratize access in Sri\u0026ensp;Lanka\u0026rsquo;s surveying industry, especially benefiting small-scale practitioners and rural projects. Emphasizing LEO satellite constellations (e.g., Starlink) is crucial. Furthermore, research into making PPP-B2b more future-proof through faster\u0026ensp;correction dissemination and increased availability of GNSS signals in obstructed environments is needed. These processes will bridge the gap between the current\u0026ensp;capabilities of PPP-B2b and the precision demands of various geospatial applications in Sri Lanka.\u003c/p\u003e\n\u003cp\u003eDespite these challenges, PPP-B2b presents a low-cost, scalable alternative to\u0026ensp;classical GNSS approaches. By reducing operational costs and infrastructure barriers through detachment from local reference networks, precise positioning becomes more accessible for Sri Lanka\u0026rsquo;s surveying sector. Its real-time capability is critical for time sensitive applications such as disaster management and navigation, but signal stability in challenging terrains remains a concern.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on this study, the authors believe that PPP-B2b is a suitable and low-cost RTK solution for real-time positioning of geospatial applications in Sri Lanka, capable of delivering centimeter to sub-meter accuracies without ground-based infrastructure. Its convergence time of 10 to 15 minutes makes it unsuitable for immediate high-precision tasks (e.g., legal\u0026ensp;surveys); PPP-B2b achieves stable, reliable accuracy within the region following convergence, which makes it powerful for large-scale or remote survey applications where millimeter-level precision is not critical. Multi-GNSS integration, hybrid systems (PPP-B2b\u0026ensp;with RTK or DGNSS at convergence), and Inertial Navigation Systems (INS) are tools that help mitigate environmental challenges like signal obstructions and ionospheric disturbances and improve stability.\u003c/p\u003e \u003cp\u003eWhile PPP-B2b is not a substitute for RTK in ultra-high-precision use cases, the operational sensor flexibility offered by PPP-B2b, along with scalability and lower infrastructure costs, represents substantial advantages\u0026ensp;for the surveying, infrastructure development, and disaster management sectors in Sri Lanka. The continued development of multi-constellation support, convergence algorithms, and cost-effective receivers will cement its place in contemporary geospatial processes, providing a bridge between accessibility\u0026ensp;and accuracy in resource-constrained scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.D.G.R. conducted the research, performed the data collection and analysis, and wrote the main manuscript text. P.H.M.I. the research, contributed to the methodology, and reviewed and edited the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI extend my sincere gratitude to my research supervisor, Prof. H.M.I. Prasanna, for his invaluable guidance, insightful advice, and unwavering support throughout this research journey. His expertise and encouragement have been instrumental in shaping the direction and success of this study. I express my heartfelt gratitude to Mr. Nissanka de Silva, Managing Director, Global GIS Pvt. Ltd. His support in providing essential equipment, data and other critical resources played a crucial role in conducting this study. I am also grateful to his team for their dedication and seamless coordination during the data collection process. Their efforts in ensuring proper fieldwork and logistical support were instrumental in maintaining the accuracy and reliability of this research. I also extend my appreciation to all the faculty members of the Department of Surveying and Geodesy, whose guidance and constructive feedback have enriched my academic experience. Additionally, I am grateful to my colleagues and friends for their collaboration, motivation, and encouragement throughout this research process. Finally, I must express my deepest appreciation to my family, whose unwavering support and love have sustained me not only during this research but throughout my life. Their encouragement has been my constant source of strength. Thank you to everyone who contributed to this effort, whether directly or indirectly. Your support has been indispensable in the completion of this thesis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAfifi, A., El-Rabbany, A., 2015. Performance analysis of several GPS/Galileo precise point positioning models. Sensors (Switzerland) 15, 14701\u0026ndash;14726. https://doi.org/10.3390/s150614701\u003c/li\u003e\n\u003cli\u003eChen, H.C., Huang, Y.S., Chiang, K.W., Yang, M., Rau, R.J., 2009. The performance comparison between GPs and BeiDou-2/compass: A perspective from Asia. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch\u0026rsquo;eng Hsuch K\u0026rsquo;an 32, 679\u0026ndash;689. https://doi.org/10.1080/02533839.2009.9671550\u003c/li\u003e\n\u003cli\u003eChen, J., Zhao, X., Liu, C., Zhu, S., Liu, Z., Yue, D., 2021. Evaluating the Latest Performance of Precise Point Positioning in Multi-GNSS/RNSS: GPS, GLONASS, BDS, Galileo and QZSS. Journal of Navigation 74, 247\u0026ndash;267. https://doi.org/10.1017/S0373463320000508\u003c/li\u003e\n\u003cli\u003eKan, H., Hu, Z., Chen, G., Liu, X., Liu, C., Zhao, Q., 2024. Performance comparison of orbit and clock augmentation corrections from PPP-B2b, HAS and CLAS. Advances in Space Research 74, 668\u0026ndash;681. https://doi.org/10.1016/j.asr.2024.04.029\u003c/li\u003e\n\u003cli\u003eKouba, J., H\u0026eacute;roux, P., 2001. Precise Point Positioning Using IGS Orbit and Clock Products. GPS Solutions 5, 12\u0026ndash;28. https://doi.org/10.1007/PL00012883\u003c/li\u003e\n\u003cli\u003eLi, R., Zheng, S., Wang, E., Chen, J., Feng, S., Wang, D., Dai, L., 2020. Advances in BeiDou Navigation Satellite System (BDS) and satellite navigation augmentation technologies. Satellite Navigation. https://doi.org/10.1186/s43020-020-00010-2\u003c/li\u003e\n\u003cli\u003eLi, R., Zheng, S., Wang, E., Chen, J., Feng, S., Wang, D., Dai, L., 2020. Advances in BeiDou Navigation Satellite System (BDS) and satellite navigation augmentation technologies. Satellite Navigation. https://doi.org/10.1186/s43020-020-00010-2\u003c/li\u003e\n\u003cli\u003eLi, X., Barriot, J.P., Lou, Y., Zhang, W., Li, P., Shi, C., 2023. Towards Millimeter-Level Accuracy in GNSS-Based Space Geodesy: A Review of Error Budget for GNSS Precise Point Positioning. Surv Geophys. https://doi.org/10.1007/s10712-023-09785-w\u003c/li\u003e\n\u003cli\u003eLiu, Y., Yang, C., Zhang, M., 2022. Comprehensive Analyses of PPP‐B2b Performance in China and Surrounding Areas. Remote Sens (Basel) 14. https://doi.org/10.3390/rs14030643\u003c/li\u003e\n\u003cli\u003eLu, J., Guo, X., Su, C., 2020. Global capabilities of BeiDou Navigation Satellite System. Satellite Navigation 1. https://doi.org/10.1186/s43020-020-00025-9\u003c/li\u003e\n\u003cli\u003eLu, X., Chen, L., Shen, N., 2020a. Decoding PPP Corrections from BDS B2b Signals Using a Software-defined Receiver: an Initial Performance Evaluation, IEEE Sensors Journal. In Press.\u003c/li\u003e\n\u003cli\u003eNie, Z., Xu, X., Wang, Z., Du, J., 2021a. Initial assessment of BDS PPP-B2b service: Precision of orbit and clock corrections, and PPP performance. Remote Sens (Basel) 13. https://doi.org/10.3390/rs13112050\u003c/li\u003e\n\u003cli\u003eRen, Z., Gong, H., Peng, J., Tang, C., Huang, X., Sun, G., 2021a. Performance assessment of real-time precise point positioning using BDS PPP-B2b service signal. Advances in Space Research 68, 3242\u0026ndash;3254. https://doi.org/10.1016/j.asr.2021.06.006\u003c/li\u003e\n\u003cli\u003eT10Pro GNSS Receiver_Real-time kinematic (RTK)_GNSS Receiver_Products_TOKNAV. (n.d.). Retrieved June 2, 2025, from https://www.toknav.cn/index.php?c=show\u0026amp;id=39\u003c/li\u003e\n\u003cli\u003eWang, J., Shi, C., Zheng, F., Yang, C., Liu, X., Liu, S., Xia, M., Jing, G., Li, T., Chen, W., Li, Q., Hu, Y., Tian, Y., Shan, Y., 2024. Multi-frequency smartphone positioning performance evaluation: insights into A-GNSS PPP-B2b services and beyond. Satellite Navigation 5. https://doi.org/10.1186/s43020-024-00146-5\u003c/li\u003e\n\u003cli\u003eWang, X., Zhou, K., Zhang, J., Li, H., Liang, H., Tu, M., Chen, Y., Qiu, C., Li, L., 2024. An investigation of PPP-B2b coverage and its performance in ZTD estimation and positioning in different regions. Survey Review. https://doi.org/10.1080/00396265.2024.2401665\u003c/li\u003e\n\u003cli\u003eWei, H., Xiao, G., Zhou, P., Li, P., Xiao, Z., Zhang, B., 2025. Combining Galileo HAS and Beidou PPP-B2b with Helmert coordinate transformation method. GPS Solutions 29. https://doi.org/10.1007/s10291-024-01789-2\u003c/li\u003e\n\u003cli\u003eYang, H., Ren, X., Liu, M., Zhang, X., 2024. Dual-frequency to five-frequency real-time precise point positioning using new BDS-3 PPP-B2b service. Earth, Planets and Space 76. https://doi.org/10.1186/s40623-024-02031-6\u003c/li\u003e\n\u003cli\u003eYang, Y., Mao, Y., Sun, B., 2020a. Basic performance and future developments of BeiDou global navigation satellite system. Satellite Navigation 1. https://doi.org/10.1186/s43020-019-0006-0\u003c/li\u003e\n\u003cli\u003eYu, Z., Pan, G., Zuo, X., Tang, Y., 2022a. Performance Analysis of Real-Time Precise Point Positioning Based on PPP-B2b Signal, in: Lecture Notes in Electrical Engineering. Springer Science and Business Media Deutschland\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Real-time PPP-B2b, Precise point positioning, Convergence time, BDS-3","lastPublishedDoi":"10.21203/rs.3.rs-6863905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe BeiDou Navigation Satellite System (BDS-3) provides a real-time precise point positioning (PPP) solution with its BDS PPP-B2b signal, which does not rely on local base stations, and therefore has become a promising solution for GNSS-based surveying applications in real-time. The present study assesses the accuracy, convergence duration and environmental impact of PPP-B2b in the case of Sri Lanka in comparison with traditional post-processed GNSS approaches such as Differential Global Navigation Satellite System (DGNSS), Single Point Positioning (SPP), and PPP-Static. The study was carried out in three different sites Maharagama (open field), Thalangama (semi-urban, adjacent to a body of water) and Diyagama (dense urban area) to evaluate PPP-B2b’s performance on varying terrain-different environmental conditions. The results demonstrate that PPP-B2b allows sub-meter positioning with an average horizontal accuracy of 0.4 m and vertical accuracy of 2.3 m, and the open-field site (Maharagama) experiences the best accuracy due to geomorphological factors and complete satellite visibility, whereas Thalangama gave moderate deviations due to multipath generated from reflections on the water. Diyagama site had the least accuracy, because of signal obstruction, urban canyon and increasing the convergence time. Even though PPP-B2b is a low-cost and independent solution that does not require terrestrial infrastructure, it is limited by several factors that result in high convergence time (typically up to 10-15 minutes) and errors generated through multipath and ionospheric delay. These limitations notwithstanding, satellite-based PPP services such as PPP-B2b are still viable options for real-time positioning and atmospheric applications that do not require internet connectivity.\u003c/p\u003e","manuscriptTitle":"Accuracy analysis of BDS-3 PPP-B2b precise point positioning over Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 15:35:54","doi":"10.21203/rs.3.rs-6863905/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e51ed92b-9547-4e6c-b38d-0daaaffd503b","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T19:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-20 15:35:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6863905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6863905","identity":"rs-6863905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}