PPP_Mansoura: an open-source software for multi-constellation GNSS processing
-
Islam A. Kandil
,
Ahmed A. Awad
Abstract
PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results.
Acknowledgments
Many thanks are owing to International GNSS Service for providing the GNSS data.
-
Author contributions: The first author analyzed and wrote the introduction, methodology, and results, while the second author steered the preparation of the document and validation, and the third author the creator of the basic concept oversaw all elements and authored conclusions and recommendations and pursue with corresponding authors to ensure the completeness and verification procedure.
-
Competing interests: All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Kandil, IA, Awaad, ME-MA. Analysis of GNSS accuracy of relative positioning and precise positioning based on online service. Int J Sci Eng Res 2017;5:94–103.Search in Google Scholar
2. Hamza, V, Stopar, B, Sterle, O, Pavlovčič-Prešeren, P. A cost-effective GNSS solution for continuous monitoring of landslides. Rem Sens 2023;15:2287. https://doi.org/10.3390/rs15092287.Search in Google Scholar
3. Huang, G, Du, S, Wang, D. GNSS techniques for real-time monitoring of landslides: a review. Sat Nav 2023;4:5. https://doi.org/10.1186/s43020-023-00095-5.Search in Google Scholar
4. Li, X, Wang, X, Liao, J, Li, X, Li, S, Lyu, H. Semi-tightly coupled integration of multi-GNSS PPP and S-VINS for precise positioning in GNSS-challenged environments. Sat Nav 2021;2:1–14. https://doi.org/10.1186/s43020-020-00033-9.Search in Google Scholar
5. Bahadur, B, Nohutcu, M. PPPH: a MATLAB-based software for multi-GNSS precise point positioning analysis. GPS Solut 2018;22:113. https://doi.org/10.1007/s10291-018-0777-z.Search in Google Scholar
6. Xiao, G, Liu, G, Ou, J, Liu, G, Wang, S, Guo, A. MG-APP: an open-source software for multi-GNSS precise point positioning and application analysis. GPS Solut 2020;24:1–13. https://doi.org/10.1007/s10291-020-00976-1.Search in Google Scholar
7. Ssenyunzi, RC, Oruru, B, D’ujanga, FM, Realini, E, Barindelli, S, Tagliaferro, G, van de Giesen, N. Variability and accuracy of Zenith Total Delay over the East African tropical region. Adv Space Res 2019;64:900–20.10.1016/j.asr.2019.05.027Search in Google Scholar
8. Chen, Z. GDP: an open-source GNSS data preprocessing toolkit. GPS Solut 2020;24:1–11.10.1007/s10291-020-01003-zSearch in Google Scholar
9. Mirmohammadian, F, Asgari, J, Verhagen, S, Amiri-Simkooei, A. Improvement of multi-gnss precision and success rate using realistic stochastic model of observations. Rem Sens 2022;14:60. https://doi.org/10.3390/rs14010060.Search in Google Scholar
10. Yu, D, Ji, B, Liu, Y, Wu, S, Li, H, Bian, S. Performance assessment of RTPPP positioning with SSR corrections and PPP-AR positioning with FCB for multi-GNSS from MADOCA products. Adv Space Res 2023;71:2924–37. https://doi.org/10.1016/j.asr.2022.11.039.Search in Google Scholar
11. Liu, X, Jiang, W, Li, P, Deng, Z, Ge, M, Schuh, H. An extended inter-system biases model for multi-GNSS precise point positioning. Measurement 2023;206:112306. https://doi.org/10.1016/j.measurement.2022.112306.Search in Google Scholar
12. Su, K, Jiao, G. Two modified multi-frequency GNSS approaches to estimate the pseudorange observable-specific signal bias for the CDMA and FDMA models. GPS Solut 2023;27:3. https://doi.org/10.1007/s10291-023-01417-5.Search in Google Scholar
13. Su, K, Jin, S, Hoque, MM. Evaluation of ionospheric delay effects on multi-GNSS positioning performance. Rem Sens 2019;11:171. https://doi.org/10.3390/rs11020171.Search in Google Scholar
14. Lu, Y, Yang, H, Li, B, Li, J, Xu, A, Zhang, M. Analysis of characteristics for inter-system bias on multi-GNSS undifferenced and uncombined precise point positioning. Rem Sens 2023;15:2252. https://doi.org/10.3390/rs15092252.Search in Google Scholar
15. Saastamoinen, J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. The use of artificial satellites for geodesy: [third international symposium], April 15-17 1972. Washington, D.C., USA,: Geophysics Monograph Service, vol. 15:247–51 pp.10.1029/GM015p0247Search in Google Scholar
16. Böhm, J, Niell, A, Tregoning, P, Schuh, H. Global Mapping Function (GMF): a new empirical mapping function based on numerical weather model data. Geophys Res Lett 2006;33:L07304. https://doi.org/10.1029/2005gl025546.Search in Google Scholar
17. Qu, Z, Guo, J, Zhao, Q. Phase center corrections for BDS IGSO and MEO satellites in IGb14 and IGSR3 frame. Rem Sens 2021;13:745. https://doi.org/10.3390/rs13040745.Search in Google Scholar
18. Wang, J, Ge, M, Glaser, S, Balidakis, K, Heinkelmann, R, Schuh, H. Improving VLBI analysis by tropospheric ties in GNSS and VLBI integrated processing. J Geod 2022;96:32. https://doi.org/10.1007/s00190-022-01615-y.Search in Google Scholar
19. Kandil, I, Awad, A, El-Mewafi, M. Role of multi-constellation GNSS in the mitigation of the observation errors and the enhancement of the positioning accuracy. Int J Geoinformatics 2023;19:25–35.10.52939/ijg.v19i4.2631Search in Google Scholar
20. Kouba, J. A guide to using International GNSS Service (IGS) products. Canada: Geodetic Survey Division Natural Resources; 2009.Search in Google Scholar
21. Wu, J-T, Wu, SC, Hajj, GA, Bertiger, WI, Lichten, SM. Effects of antenna orientation on GPS carrier phase. Astrodynamics 1992:1647–60.Search in Google Scholar
22. Petit, G, Luzum, B. The 2010 reference edition of the IERS conventions. In: Reference frames for applications in geosciences. Canada: Springer; 2013:57–61 pp.10.1007/978-3-642-32998-2_10Search in Google Scholar
23. Noll, CE. The crustal dynamics data information system: a resource to support scientific analysis using space geodesy. Adv Space Res 2010;45:1421–40. https://doi.org/10.1016/j.asr.2010.01.018.Search in Google Scholar
24. Alcay, S, Turgut, M. Evaluation of the positioning performance of multi-GNSS RT-PPP method. Arabian J Geosci 2021;14:1–19. https://doi.org/10.1007/s12517-021-06534-4.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
Articles in the same Issue
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
