A Comparative Analysis of Trimble R8 GNSS Receiver and Catalyst System for point coordination

A Comparative Analysis of Trimble R8 GNSS Receiver and Catalyst System for point coordination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Comparative Analysis of Trimble R8 GNSS Receiver and Catalyst System for point coordination odesola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9266374/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 Global Navigation Satellite Systems (GNSS) has brought about significant changes in the methods of positioning and navigation. Two different GNSS systems were compared in this study,the Differential GNSS (DGNSS) and the precise point positioning (PPP) techniques. The Trimble catalyst system served as the precise point positioning system and the Trimble R8 GNSS with the CORS applied as base station representing DGNSS. Goal and Objectives: The study aims at carry out a comparison in analysis between the Trimble catalyst system using the RTX correction service and the Trimble R8 GNSS Receiver with the Continuously Operating Reference station (CORS) which is achieved through acquiring GNSS data from the study area using already established control, t o carry out statistical analysis to verify the reliability of results and to test if RTX correction service is a reliable from of method for obtaining GNSS gotten data. Methodology: Spatial coordinates of some selected points were acquired with the use of Global Navigation Satellite Systems (GNSS) receivers and post processed using Trimble Business Centre. These results were then compared to see if there is any significant difference using the paired T-test statistics on the SPSS Software Package and recommendations were made based on the results gotten. Results: The Catalyst system on one hand had the advantage due to its portability and modularity concept behind its design but the subscription price to the RTX correction depending on the level of precision paid for, was on the high side in terms of price when compared to the cost of using the CORS data that is open sourced. But this could be solved if there is more integration among members who own the catalyst system in purchasing and all sharing a subscription account based on agreement. It was then concluded that although the R8 is a better equipment the catalyst system could also be adopted in case of absence of a DGNSS equipment like the Trimble R8 and the catalyst used in its place since the there is no significant different in the result of both. Environmental Engineering GNSS Trimble Catalyst t-test CORS Figures Figure 1 INTRODUCTION The Global Navigation Satellite System (GNSS) refers to the technical seamless integration and interaction among various satellite navigation systems, including modernized GPS, Galileo, and reconstructed GLONASS, for civilian use, irrespective of the nationalities of each system, to enhance safety, convenience, and efficiency while minimising the time required and improving reliability (Feng, 2003 ). There has been yet another significant development in surveying technology as a result of recent improvements in military navigation techniques and global positioning systems. Coordinates may be precisely located on a worldwide reference plane with the use of GNSS, and measurements made using GNSS land surveying equipment are not affected by changes to the nearby terrain, buildings, or landmarks. In order to achieve the required level of precision, GNSS mapping technology frequently employs GNSS enhancement procedures. Using Real-Time Kinematics (RTK), these options range from satellite-based augmentation systems such as EGNOS (European Geostationary Navigation Overlay Service) to dual frequency receivers. The augmentation technique is chosen depending on the amount of survey accuracy required, the supplies readily accessible, the duration needed for the survey, and the site's surrounding conditions. Most survey grade receivers employ Real Time Kinematic (RTK) or DGNSS. Typically survey grade receivers use DGNSS or Real Time Kinematic (RTK) (Madry, 2015 ; Hofmann-Wellenhof et al., 2008 ; Leick et al., 2015 ). These techniques necessitate data acquisition from a base station characterized by accurately known coordinates. Such data may be sourced from a network of base stations, a publicly available single base station, or one specifically installed by the surveyor. Surveyors can opt for either real-time corrections, which require a communication link between the base station and the rover, or post-processing corrections. In terms of cost, advanced GNSS surveying equipment generally surpasses traditional surveying instruments. However, for large-scale topographic surveys where centimeter-level precision is necessary, the increased cost of GNSS technology becomes negligible, given its capacity to significantly accelerate the survey process compared to classical approach (Roy, 2008 ). The Trimble Catalyst system is engineered to provide a simple and quick setup procedure. This system enables users to utilise Spatial coordination placement as a service, facilitating the acquisition of geographical information via Trimble or third-party applications on smartphones, tablets, and mobile portable devices. Integrating the receiver with a simple to use digital antenna and connecting to the Catalyst service enables normal Android devices to function as centimeter-accurate data gathering systems. The solution just needs Trimble's small and light DA1 antenna, which connects straight to Android mobile devices, and a Catalyst service which offers accuracy choices ranging from one meter to millimetre resolution. (Trimble, 2017). Precise point positioning (PPP) approach has been appealing since it offers great result with the use of remote station infrastructure especially by the geospatial mapping and Geodesy community. The international service community and various analysis centres now offer live accurate satellite coordinate placement and clock products (Li., 2021). The Trimble R8 is a crucial GNSS companion for surveyors dealing with challenging RTK applications. The Trimble R8 offers a reliable option for surveyors and can track signal transmissions from multiple constellations. Furthermore, it is possible to eliminate unnecessary missions by downloading post-processing data via a web user interface. (TRIMBLE, 2010). However, the use of mobile devices as receivers to capture data with external antenna and app is one that is fascinating and brings curiosity as to whether this technology is as good and reliable as an alternative to the normal GNSS receiver especially with the innovation of the precise point positioning (PPP) technology, whereby there is no need to set up base stations but now base stations can be accessed from remote places, which can be used to produce live coordinate production (Alkan et al., 2004). This is why these two products of the same manufacturer are being compared and their data analyzed to see if the correlation is one that matches the other. For all DGNSS techniques, coordinate placement is carried out using one or more positioning receivers located at known coordinate coordinates. Various degrees of performance can be obtained dependent on the measurement type, devices, user budget, processing algorithm, secondary connections, and condition of processing connection, accurate satellite positioning and clock products (El-Rabbany, 2002 ; Kouadio & Davy, 2016 ). RTK methods are increasingly essential for accurate mapping, surveying, machine automation, and navigation (Ghilani and wolf, 2012 ). RTK techniques are becoming increasingly important for precise mapping, surveying, machine automation, and navigation (Ghilani and Wolf, 2012 ). CORSs, or continuously operating reference stations, have been set up to service RTK customers. Creating and sustaining this spatial coordinate establishment structures at the appropriate degree, ability in terms of result, capability (e.g., dual frequency), signal quality and reference plane metrics, between this infrastructure and consumers is demanding and on the high side. This has necessitated the Precise Point Positioning (PPP) technique which is being presented as an alternative to the huge conditional set up CORS systems. PPP employs mobile devices to achieve positioning with milimeter level result (Katrin et al., 2010 ). This study suggests a practical combination of inexpensive GNSS to bridge some of these gaps. Despite the fact that PPP has a number of drawbacks, including delayed convergence times, lack of user infrastructure and live data log process, a lack of real-time satellite orbit and clock data streams, and imprecise reference plane, current advances may be able to overcome the majority of these drawbacks. (Rizos et al., 2012 ). MATERIALS AND METHODS This section gives an overview of the used data, methodology, their significance, source and explanation of the operations done, all in a view to arrive at the stated objectives. Hardware Trimble R8 GNSS Receiver DL1 Antenna Android phone with requirements with; Certificate USB on-The-Go Have an operating system of Android 5.0 or greater Have more than 1.4 GB of RAM Have a CPU of at least 1.4 GHz and 4 or more cores. Ranging/Fiber pole HP Laptop Computer with its accessories. Tripod Software a. GPS processor: it will be used for the processing of the GPS acquired data. (Trimble Business Centre and Trimble Penmap Android App). b. AutoCAD: This will be used for the processing and presentation of acquired data. c. Microsoft Word: This software will be used in preparing the project reports d. Microsoft EXCEL e. SPSS software - for carrying out analysis. DATA SOURCE The primary data source for this project is through field observation, it involved the collection of X, Y, Z coordinates of the features (control points) used. The secondary data source was an imagery of the area was acquired through Google earth; to visualize the project area. Static method was used for the R8 receiver method and the Penmap used the precise point method. 4.1 PRESENTATION OF RESULTS Table 1 – Table Showing the Processed R8 Data of the TBC Software ID Easting (Meter) Northing (Meter) Elevation (Meter) TRIM199 CORS 538335.104 729878.477 35.477 YTT28/4 539335.246 730997.939 37.151 ZTT45/56 539312.892 730921.239 19.859 ZTT45/36 538999.874 730548.929 30.730 XST 118 538250.104 729990.477 35.477 Table 2 – Table Showing the Processed R8 Data of the Catalyst System ID Easting (Meter) Northing (Meter) Elevation (Meter) Horizontal Precision Vertical Precision YTT28/4 539335.925 730997.074 37.787 27.4CM 42.5CM ZTT45/56 539312.57 730920.729 18.585 24.4CM 34.2CM ZTT45/36 538999.095 730548.427 35.647 26.5CM 38.4CM XST 118 538250.763 729991.278 34.842 28.9 33.7 4.2 ANALYSIS OF RESULT Statistical Analysis was carried out to test how reliable the data are in form of the coordinates obtained by the receivers. The following hypothesis was tested by means of one-way paired T-TEST using the SPSS version 22 software. Table 1 - Table 6 show the data obtained for all the observed points and the differences at each instance. 1. NORTHINGS HYPOTHESIS Null hypothesis: H o : there is no significant difference between northings coordinates recorded by the receivers used. Alternative hypothesis: H 1 : there is difference between the northings coordinates recorded by the receivers used. 2. EASTINGS HYPOTHESIS Null hypothesis: H o : there is no difference between Eastings coordinates recorded by the receivers used. Alternative hypothesis: H 1 : there is difference between the Eastings coordinates recorded by the receivers used. 3. HEIGHT HYPOTHESIS Null hypothesis: H o : there is no difference between height coordinates recorded by the receivers used. Alternative hypothesis: H 1 : there is difference between the height coordinates recorded by the receivers used. Table 3 – Table Showing Northings ID R8 CATALYST YTT28/4 730997.939 730997.074 ZTT45/56 730921.239 730920.729 ZTT45/36 730548.929 730548.427 XST 118 729990.477 729991.278 Table 4 – Table Showing the Eastings ID R8 CATALYST YTT28/4 539335.104 539335.925 ZTT45/56 539335.246 539312.57 ZTT45/36 538999.874 538999.095 XST 118 539078.874 538250.763 Table 5 – Table Showing the Elevations ID R8 CATALYST YTT28/4 37.151 37.787 ZTT45/56 19.859 18.585 ZTT45/36 30.730 35.647 XST 118 35.477 34.842 Table 6 – Table Showing the Difference dx dy dz -0.679 0.865 -0.636 0.322 0.51 1.274 0.779 0.502 -4.917 -0.659 -0.801 0.635 WHERE dx – difference in eastings, dy - difference in northings, dz - difference in height T-TEST RESULTS Table 7: Paired Samples Correlations Mean N Std. Deviation Std. Error Mean Pair 1 R8_EASTINS 538994.27900 4 509.485192 254.742596 CATALYST_EASTING 538974.58825 4 506.398882 253.199441 Pair 2 R8_NORTHING 730614.64600 4 460.006687 230.003343 CATALYST_NORTHTING 730614.37700 4 459.314819 229.657409 Pair 3 R8_HEIGHT 30.80425 4 7.787165 3.893583 CATALYST_HEIGHT 31.71525 4 8.841283 4.420641 N Correlation Sig. Level Pair 1 R8_EASTINS & CATALYST_EASTING 4 .997 .003 Pair 2 R8 NORTHING & CATALYST_NORTHINGS 4 1.000 .000 Pair 3 R8_HEIGHT & CATALYST_HEIGHT 4 .952 .048 T-TEST DECISION RULES If the p-value < 0.05, which is the confidence interval reject H o , if not do not reject; where p-value = sig. (2-tailed). Pair 1 :( p-value) = 0.398, Since 0.398 0.05, we do not reject, Hence H 0 is accepted for pair 1. Pair 2 :( p-value) = 0.516, Since 0.398 0.05, we do not reject, Hence H 0 is accepted for pair 1. Pair 3 :( p-value) – 0.560, Since 0.560 0.05, we do not reject, Hence H 0 is accepted for pair 1. Conclusively, for all pairs, it is seen that the H o , i.e. the null hypothesis is accepted in all cases since it is greater than 95% confidence interval. So in all cases of the Northings, Eastings, and height coordinates, the results obtained as shown in Table 7 showed no significant difference. This means that the catalyst data were satisfactory enough to match up with the R8 result for the operations on field, although, the R8 gave a better result than the catalyst. CONCLUSION AND RECOMMENDATIONS The study has attempted to carry out comparison between results gotten from two GNSS system of equipments and deduce the significant difference between them and as a result produce inferences from them. The Catalyst system on one hand had the advantage due to its portability and modularity concept behind its design but the subscription price to the RTX correction depending on the level of precision paid for, was on the high side in terms of price when compared to the cost of using the CORS data that is open sourced. But this could be solved if there is more integration among members who own the catalyst system in purchasing and all sharing a subscription account based on agreement. The main challenge associated with the Precise Point Positioning (PPP) technique is the extended duration of observations required to achieve centimeter-level accuracy, which can often exceed thirty minutes. This limitation makes PPP less suitable for real-time applications where immediate positioning is critical. In contrast, when satellite visibility is compromised or when there is insufficient local control quality, PPP can provide users with accurate and reliable positioning data that may not be accessible through traditional differential methods. Furthermore, the efficacy of PPP lies in its ability to deliver precise location information in challenging environments, such as urban canyons or heavily wooded areas, where satellite signals are obstructed. Despite its longer observation times, PPP remains a valuable tool in various applications, particularly in surveying, geodesy, and environmental monitoring. The choice between PPP and other positioning techniques ultimately depends on specific project requirements and desired accuracy levels. For instance, in projects demanding high precision but with less stringent time constraints, PPP can be an advantageous option. Conversely, for real-time positioning needs, differential techniques may be preferred due to their capability to provide immediate results. As such, it is essential to evaluate the project's objectives, satellite conditions, and accuracy needs to determine the most suitable positioning method. Declarations Funding and Ethics Declaration Fundings The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests (Conflict of Interest) The author has no relevant financial or non-financial interests to disclose. Ethical Approval This study was conducted in accordance with the Declaration mentioned. Informed Consent Informed consent was obtained from all individual participants included in the study. References Alkan R.M., Ilci V. and I. Ozulu M. (2016). Web-based GNSS Data Processing Services as an Alternative to Conventional Processing Technique, Conference Paper · May 2016, FIG Working Week 2016, Recovery from Disaster, Christchurch, New Zealand, May 2–6, 2016. Bisnath S., Gao Y., Current state of precise point positioning and future prospects and limitations. Proceedings of IUGG, 24th General Assembly. DATASHEET FOR TRIMBLE CATALYST SOFT GNSS SOLUTIONS. COPYRIGHT 2017. WWW.TRIMBLE.COM El-Rabbany A. (2002). Introduction to GPS, the Global Positioning System”, Artech House communication series, 2002; Library of Congress, United States of America. Feng, Y. (2003). GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals. J Geod 82 , 847–862 (2008). https://doi.org/10.1007/s00190-008-0209-x Ghilani C.D., Wolf P.R. (2012). Elementary Surveying: An Introduction to Geomatics (2012), Pearson Education. Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2008). GNSS – Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer. Katrin H., Florian H., Christoph A., Ana K., Robert W., Philipp B., (2010). PPP: Precise Point Positioning- Contrasts and Opportunities, XXIV FIG International Congress 2010 pp. 1-17. Kouadio, K., & Davy, A. (2016). Differential GPS: A Survey of Applications and Techniques. In Proceedings of the International Conference on Intelligent Robotics and Applications (pp. 158-167). Springer. Leick, A., Rapoport, L., & Tatarnikov, D. (2015). GPS satellite surveying (4th ed.). John Wiley & Sons. Li, W. (2021). Assessment of multi-GNSS precise orbit and clock products from different analysis centers based on precise point positioning. Acta Geodynamica Et Geomaterialia, 387–397. https://doi.org/10.13168/agg.2021.0027 Madry S., 2015. Global Navigation Satellite Systems and Their Applications, New York: Springer – Verlag New York. Rizos, Chris & Janssen, Volker & Roberts, C & Grinter, T. (2012). Precise Point Positioning: Is the era of differential GNSS positioning drawing to an end?. Roy S. K. (2008). “Fundamentals of surveying” 7 th Edition, PHI Learning Private Ltd., New Delhi. Wafeek Ismail (2016). Evaluating the Differences and Accuracies between GNSS Applications Using PPP, 2016. Zhiyu W., Zishen L. & Liang W., Xiaoming W. ID and Hong Y. (2018). Assessment of Multiple GNSS Real-Time SSR Products from Different Analysis Centers, International Journal of Geo-Information Article 2018. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9266374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614542096,"identity":"fc05b512-19f0-4e3e-9063-878d38b7c292","order_by":0,"name":"odesola","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-0067-8608","institution":"Bowen University","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"odesola","suffix":""}],"badges":[],"createdAt":"2026-03-30 11:45:44","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-9266374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9266374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105886489,"identity":"0c40eef6-28b3-4874-bac1-7f53ee82073c","added_by":"auto","created_at":"2026-04-01 07:29:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12761,"visible":true,"origin":"","legend":"\u003cp\u003eBar Chart Showing the Differences\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9266374/v1/be3fab5056089835dc9eb2e1.png"},{"id":105886547,"identity":"1c8b0645-4972-41f8-90b5-d2aeced2ae54","added_by":"auto","created_at":"2026-04-01 07:29:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":765551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9266374/v1/63275a76-68f4-42c4-b8ed-3c89c0ab8765.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Comparative Analysis of Trimble R8 GNSS Receiver and Catalyst System for point coordination\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Global Navigation Satellite System (GNSS) refers to the technical seamless integration and interaction among various satellite navigation systems, including modernized GPS, Galileo, and reconstructed GLONASS, for civilian use, irrespective of the nationalities of each system, to enhance safety, convenience, and efficiency while minimising the time required and improving reliability (Feng, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere has been yet another significant development in surveying technology as a result of recent improvements in military navigation techniques and global positioning systems. Coordinates may be precisely located on a worldwide reference plane with the use of GNSS, and measurements made using GNSS land surveying equipment are not affected by changes to the nearby terrain, buildings, or landmarks. In order to achieve the required level of precision, GNSS mapping technology frequently employs GNSS enhancement procedures. Using Real-Time Kinematics (RTK), these options range from satellite-based augmentation systems such as EGNOS (European Geostationary Navigation Overlay Service) to dual frequency receivers. The augmentation technique is chosen depending on the amount of survey accuracy required, the supplies readily accessible, the duration needed for the survey, and the site's surrounding conditions. Most survey grade receivers employ Real Time Kinematic (RTK) or DGNSS. Typically survey grade receivers use DGNSS or Real Time Kinematic (RTK) (Madry, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hofmann-Wellenhof et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Leick et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These techniques necessitate data acquisition from a base station characterized by accurately known coordinates. Such data may be sourced from a network of base stations, a publicly available single base station, or one specifically installed by the surveyor. Surveyors can opt for either real-time corrections, which require a communication link between the base station and the rover, or post-processing corrections. In terms of cost, advanced GNSS surveying equipment generally surpasses traditional surveying instruments. However, for large-scale topographic surveys where centimeter-level precision is necessary, the increased cost of GNSS technology becomes negligible, given its capacity to significantly accelerate the survey process compared to classical approach (Roy, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Trimble Catalyst system is engineered to provide a simple and quick setup procedure. This system enables users to utilise Spatial coordination placement as a service, facilitating the acquisition of geographical information via Trimble or third-party applications on smartphones, tablets, and mobile portable devices. Integrating the receiver with a simple to use digital antenna and connecting to the Catalyst service enables normal Android devices to function as centimeter-accurate data gathering systems. The solution just needs Trimble's small and light DA1 antenna, which connects straight to Android mobile devices, and a Catalyst service which offers accuracy choices ranging from one meter to millimetre resolution. (Trimble, 2017).\u003c/p\u003e \u003cp\u003ePrecise point positioning (PPP) approach has been appealing since it offers great result with the use of remote station infrastructure especially by the geospatial mapping and Geodesy community. The international service community and various analysis centres now offer live accurate satellite coordinate placement and clock products (Li., 2021). The Trimble R8 is a crucial GNSS companion for surveyors dealing with challenging RTK applications. The Trimble R8 offers a reliable option for surveyors and can track signal transmissions from multiple constellations. Furthermore, it is possible to eliminate unnecessary missions by downloading post-processing data via a web user interface. (TRIMBLE, 2010).\u003c/p\u003e \u003cp\u003eHowever, the use of mobile devices as receivers to capture data with external antenna and app is one that is fascinating and brings curiosity as to whether this technology is as good and reliable as an alternative to the normal GNSS receiver especially with the innovation of the precise point positioning (PPP) technology, whereby there is no need to set up base stations but now base stations can be accessed from remote places, which can be used to produce live coordinate production (Alkan et al., 2004). This is why these two products of the same manufacturer are being compared and their data analyzed to see if the correlation is one that matches the other.\u003c/p\u003e \u003cp\u003eFor all DGNSS techniques, coordinate placement is carried out using one or more positioning receivers located at known coordinate coordinates. Various degrees of performance can be obtained dependent on the measurement type, devices, user budget, processing algorithm, secondary connections, and condition of processing connection, accurate satellite positioning and clock products (El-Rabbany, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kouadio \u0026amp; Davy, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRTK methods are increasingly essential for accurate mapping, surveying, machine automation, and navigation (Ghilani and wolf, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). RTK techniques are becoming increasingly important for precise mapping, surveying, machine automation, and navigation (Ghilani and Wolf, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). CORSs, or continuously operating reference stations, have been set up to service RTK customers. Creating and sustaining this spatial coordinate establishment structures at the appropriate degree, ability in terms of result, capability (e.g., dual frequency), signal quality and reference plane metrics, between this infrastructure and consumers is demanding and on the high side. This has necessitated the Precise Point Positioning (PPP) technique which is being presented as an alternative to the huge conditional set up CORS systems. PPP employs mobile devices to achieve positioning with milimeter level result (Katrin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study suggests a practical combination of inexpensive GNSS to bridge some of these gaps. Despite the fact that PPP has a number of drawbacks, including delayed convergence times, lack of user infrastructure and live data log process, a lack of real-time satellite orbit and clock data streams, and imprecise reference plane, current advances may be able to overcome the majority of these drawbacks. (Rizos et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis section gives an overview of the used data, methodology, their significance, source and explanation of the operations done, all in a view to arrive at the stated objectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHardware\u003c/strong\u003e\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eTrimble R8 GNSS Receiver\u003c/li\u003e\n \u003cli\u003eDL1 Antenna \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAndroid phone with requirements with;\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003e\u0026nbsp;Certificate USB on-The-Go\u003c/li\u003e\n \u003cli\u003eHave an operating system of Android 5.0 or greater\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHave more than 1.4 GB of RAM\u003c/li\u003e\n \u003cli\u003eHave a CPU of at least 1.4 GHz and 4 or more cores.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003eRanging/Fiber pole\u003c/li\u003e\n \u003cli\u003eHP Laptop Computer with its accessories.\u003c/li\u003e\n \u003cli\u003eTripod\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. GPS processor: it will be used for the processing of the GPS acquired data. (Trimble Business Centre and Trimble Penmap Android App).\u003c/p\u003e\n\u003cp\u003eb. AutoCAD: This will be used for the processing and presentation of acquired data.\u003c/p\u003e\n\u003cp\u003ec. Microsoft Word: This software will be used in preparing the project reports\u003c/p\u003e\n\u003cp\u003ed. Microsoft EXCEL\u003c/p\u003e\n\u003cp\u003ee. SPSS software - for carrying out analysis.\u003c/p\u003e"},{"header":"DATA SOURCE","content":"\u003cp\u003eThe primary data source for this project is through field observation, it involved the collection of X, Y, Z coordinates of the features (control points) used.\u003c/p\u003e\n\u003cp\u003eThe secondary data source was an imagery of the area was acquired through Google earth; to visualize the project area. Static method was used for the R8 receiver method and the Penmap used the precise point method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 PRESENTATION OF RESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003eTable 1 \u0026ndash; Table Showing the Processed R8 Data of the TBC Software\u003c/div\u003e\n \u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEasting\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNorthing\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTRIM199 CORS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538335.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e729878.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYTT28/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539335.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730997.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e37.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539312.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730921.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e19.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538999.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730548.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e30.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXST 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538250.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e729990.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003eTable 2 \u0026ndash; Table Showing the Processed R8 Data of the Catalyst System\u0026nbsp;\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEasting\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNorthing\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003cp\u003e(Meter)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHorizontal\u003c/p\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eVertical\u003c/p\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYTT28/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539335.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730997.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e37.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e27.4CM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e42.5CM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539312.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730920.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e18.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e24.4CM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e34.2CM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538999.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730548.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e26.5CM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e38.4CM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXST 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538250.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e729991.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e34.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 ANALYSIS OF RESULT\u003c/h2\u003e\n \u003cp\u003eStatistical Analysis was carried out to test how reliable the data are in form of the coordinates obtained by the receivers. The following hypothesis was tested by means of one-way paired T-TEST using the SPSS version 22 software. Table\u0026nbsp;1 - Table\u0026nbsp;6 show the data obtained for all the observed points and the differences at each instance.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. NORTHINGS HYPOTHESIS\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNull hypothesis: H\u003csub\u003eo\u003c/sub\u003e: there is no significant difference between northings coordinates recorded by the receivers used.\u003c/p\u003e\n \u003cp\u003eAlternative hypothesis: H\u003csub\u003e1\u003c/sub\u003e: there is difference between the northings coordinates recorded by the receivers used.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. EASTINGS HYPOTHESIS\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNull hypothesis: H\u003csub\u003eo\u003c/sub\u003e: there is no difference between Eastings coordinates recorded by the receivers used.\u003c/p\u003e\n \u003cp\u003eAlternative hypothesis: H\u003csub\u003e1\u003c/sub\u003e: there is difference between the Eastings coordinates recorded by the receivers used.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. HEIGHT HYPOTHESIS\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNull hypothesis: H\u003csub\u003eo\u003c/sub\u003e: there is no difference between height coordinates recorded by the receivers used.\u003c/p\u003e\n \u003cp\u003eAlternative hypothesis: H\u003csub\u003e1\u003c/sub\u003e: there is difference between the height coordinates recorded by the receivers used.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003eTable 3 \u0026ndash; Table Showing Northings\u003c/div\u003e\n \u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCATALYST\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYTT28/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e730997.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730997.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e730921.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730920.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e730548.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e730548.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXST 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e729990.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e729991.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 4 \u0026ndash; Table Showing the Eastings\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCATALYST\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYTT28/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539335.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e539335.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539335.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e539312.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e538999.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e538999.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXST 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e539078.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e538250.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003eTable 5 \u0026ndash; Table Showing the Elevations\u003c/div\u003e\n \u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCATALYST\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYTT28/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e37.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e37.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e19.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eZTT45/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e30.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e35.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eXST 118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e35.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e34.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 \u0026ndash; Table Showing the Difference\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edx\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003edy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003edz\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e-0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-4.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e-0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHERE dx \u0026ndash; difference in eastings, dy - difference in northings, dz - difference in height\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT-TEST RESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 7: Paired Samples Correlations\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ePair 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eR8_EASTINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e538994.27900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e509.485192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e254.742596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; CATALYST_EASTING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e538974.58825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e506.398882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e253.199441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ePair 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eR8_NORTHING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e730614.64600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e460.006687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e230.003343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CATALYST_NORTHTING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e730614.37700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e459.314819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e229.657409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ePair 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eR8_HEIGHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e30.80425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e7.787165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.893583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCATALYST_HEIGHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e31.71525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e8.841283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.420641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003ePair 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eR8_EASTINS \u0026amp; CATALYST_EASTING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003ePair 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eR8 NORTHING \u0026amp; CATALYST_NORTHINGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003ePair 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eR8_HEIGHT \u0026amp; CATALYST_HEIGHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"705\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT-TEST DECISION RULES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf the p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which is the confidence interval reject H\u003csub\u003eo\u003c/sub\u003e, if not do not reject; where p-value\u0026thinsp;=\u0026thinsp;sig. (2-tailed).\u003c/p\u003e\n\u003cp\u003ePair 1 :( p-value)\u0026thinsp;=\u0026thinsp;0.398,\u003c/p\u003e\n\u003cp\u003eSince 0.398 0.05, we do not reject, Hence H\u003csub\u003e0\u003c/sub\u003e is accepted for pair 1.\u003c/p\u003e\n\u003cp\u003ePair 2 :( p-value)\u0026thinsp;=\u0026thinsp;0.516,\u003c/p\u003e\n\u003cp\u003eSince 0.398 0.05, we do not reject, Hence H\u003csub\u003e0\u003c/sub\u003e is accepted for pair 1.\u003c/p\u003e\n\u003cp\u003ePair 3 :( p-value) \u0026ndash; 0.560,\u003c/p\u003e\n\u003cp\u003eSince 0.560 0.05, we do not reject, Hence H\u003csub\u003e0\u003c/sub\u003e is accepted for pair 1.\u003c/p\u003e\n\u003cp\u003eConclusively, for all pairs, it is seen that the H\u003csub\u003eo\u003c/sub\u003e, i.e. the null hypothesis is accepted in all cases since it is greater than 95% confidence interval. So in all cases of the Northings, Eastings, and height coordinates, the results obtained as shown in Table\u0026nbsp;7 showed no significant difference. This means that the catalyst data were satisfactory enough to match up with the R8 result for the operations on field, although, the R8 gave a better result than the catalyst.\u003c/p\u003e"},{"header":"CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eThe study has attempted to carry out comparison between results gotten from two GNSS system of equipments and deduce the significant difference between them and as a result produce inferences from them.\u003c/p\u003e \u003cp\u003eThe Catalyst system on one hand had the advantage due to its portability and modularity concept behind its design but the subscription price to the RTX correction depending on the level of precision paid for, was on the high side in terms of price when compared to the cost of using the CORS data that is open sourced. But this could be solved if there is more integration among members who own the catalyst system in purchasing and all sharing a subscription account based on agreement.\u003c/p\u003e \u003cp\u003eThe main challenge associated with the Precise Point Positioning (PPP) technique is the extended duration of observations required to achieve centimeter-level accuracy, which can often exceed thirty minutes. This limitation makes PPP less suitable for real-time applications where immediate positioning is critical. In contrast, when satellite visibility is compromised or when there is insufficient local control quality, PPP can provide users with accurate and reliable positioning data that may not be accessible through traditional differential methods.\u003c/p\u003e \u003cp\u003eFurthermore, the efficacy of PPP lies in its ability to deliver precise location information in challenging environments, such as urban canyons or heavily wooded areas, where satellite signals are obstructed. Despite its longer observation times, PPP remains a valuable tool in various applications, particularly in surveying, geodesy, and environmental monitoring.\u003c/p\u003e \u003cp\u003eThe choice between PPP and other positioning techniques ultimately depends on specific project requirements and desired accuracy levels. For instance, in projects demanding high precision but with less stringent time constraints, PPP can be an advantageous option. Conversely, for real-time positioning needs, differential techniques may be preferred due to their capability to provide immediate results. As such, it is essential to evaluate the project's objectives, satellite conditions, and accuracy needs to determine the most suitable positioning method.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding and Ethics Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests (Conflict of Interest)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration mentioned.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlkan R.M., Ilci V. and I. Ozulu M. (2016). Web-based GNSS Data Processing Services as an Alternative to Conventional Processing Technique, Conference Paper \u0026middot; May 2016, FIG Working Week 2016, Recovery from Disaster, Christchurch, New Zealand, May 2\u0026ndash;6, 2016. \u003c/li\u003e\n\u003cli\u003eBisnath S., Gao Y., Current state of precise point positioning and future prospects and limitations. Proceedings of IUGG, 24th General Assembly. \u003c/li\u003e\n\u003cli\u003eDATASHEET FOR TRIMBLE CATALYST SOFT GNSS SOLUTIONS. COPYRIGHT 2017. WWW.TRIMBLE.COM\u003c/li\u003e\n\u003cli\u003eEl-Rabbany A. (2002). Introduction to GPS, the Global Positioning System\u0026rdquo;, Artech House communication series, 2002; Library of Congress, United States of America. \u003c/li\u003e\n\u003cli\u003eFeng, Y. (2003). GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals. \u003cem\u003eJ Geod\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 847\u0026ndash;862 (2008). https://doi.org/10.1007/s00190-008-0209-x\u003c/li\u003e\n\u003cli\u003eGhilani C.D., Wolf P.R. (2012). Elementary Surveying: An Introduction to Geomatics (2012), Pearson Education.\u003c/li\u003e\n\u003cli\u003eHofmann-Wellenhof, B., Lichtenegger, H., \u0026amp; Wasle, E. (2008). GNSS \u0026ndash; Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer.\u003c/li\u003e\n\u003cli\u003eKatrin H., Florian H., Christoph A., Ana K., Robert W., Philipp B., (2010). PPP: Precise Point Positioning- Contrasts and Opportunities, XXIV FIG International Congress 2010 pp. 1-17. \u003c/li\u003e\n\u003cli\u003e\u003cem\u003eKouadio, K., \u0026amp; Davy, A. (2016). Differential GPS: A Survey of Applications and Techniques. In Proceedings of the International Conference on Intelligent Robotics and Applications (pp. 158-167). Springer.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eLeick, A., Rapoport, L., \u0026amp; Tatarnikov, D. (2015). \u003cem\u003eGPS satellite surveying\u003c/em\u003e (4th ed.). John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eLi, W. (2021). Assessment of multi-GNSS precise orbit and clock products from different analysis centers based on precise point positioning. Acta Geodynamica Et Geomaterialia, 387\u0026ndash;397. https://doi.org/10.13168/agg.2021.0027\u003c/li\u003e\n\u003cli\u003eMadry S., 2015. Global Navigation Satellite Systems and Their Applications, New York: Springer \u0026ndash; Verlag New York. \u003c/li\u003e\n\u003cli\u003eRizos, Chris \u0026amp; Janssen, Volker \u0026amp; Roberts, C \u0026amp; Grinter, T. (2012). Precise Point Positioning: Is the era of differential GNSS positioning drawing to an end?. \u003c/li\u003e\n\u003cli\u003eRoy S. K. (2008). \u0026ldquo;Fundamentals of surveying\u0026rdquo; 7\u003csup\u003eth\u003c/sup\u003e Edition, PHI Learning Private Ltd., New Delhi. \u003c/li\u003e\n\u003cli\u003eWafeek Ismail (2016). Evaluating the Differences and Accuracies between GNSS Applications Using PPP, 2016.\u003c/li\u003e\n\u003cli\u003eZhiyu W., Zishen L. \u0026amp; Liang W., Xiaoming W. ID and Hong Y. (2018). Assessment of Multiple GNSS Real-Time SSR Products from Different Analysis Centers, International Journal of Geo-Information Article 2018. \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":false,"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":"GNSS, Trimble Catalyst, t-test, CORS","lastPublishedDoi":"10.21203/rs.3.rs-9266374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9266374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal Navigation Satellite Systems (GNSS) has brought about significant changes in the methods of positioning and navigation. Two different GNSS systems were compared in this study,the Differential GNSS (DGNSS) and the precise point positioning (PPP) techniques. The Trimble catalyst system served as the precise point positioning system and the Trimble R8 GNSS with the CORS applied as base station representing DGNSS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGoal and Objectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aims at carry out a comparison in analysis between the Trimble catalyst system using the RTX correction service and the Trimble R8 GNSS Receiver with the Continuously Operating Reference station (CORS) which is achieved through acquiring GNSS data from the study area using already established control, \u003cstrong\u003e\u0026nbsp;t\u003c/strong\u003eo carry out statistical analysis to verify the reliability of results and to test if RTX correction service is a reliable from of method for obtaining GNSS gotten data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial coordinates of some selected points were acquired with the use of \u0026nbsp;Global Navigation Satellite Systems (GNSS) receivers and post processed using Trimble Business Centre. These results were then compared to see if there is any significant difference using the paired T-test statistics on the SPSS Software Package and recommendations were made based on the results gotten.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Catalyst system on one hand had the advantage due to its portability and modularity concept behind its design but the subscription price to the RTX correction depending on the level of precision paid for, was on the high side in terms of price when compared to the cost of using the CORS data that is open sourced. But this could be solved if there is more integration among members who own the catalyst system in purchasing and all sharing a subscription account based on agreement. It was then concluded that although the R8 is a better equipment the catalyst system could also be adopted in case of absence of a DGNSS equipment like the Trimble R8 and the catalyst used in its place since the there is no significant different in the result of both.\u003c/p\u003e","manuscriptTitle":"A Comparative Analysis of Trimble R8 GNSS Receiver and Catalyst System for point coordination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 07:29:12","doi":"10.21203/rs.3.rs-9266374/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":"9cf201be-7b9b-454d-a0b2-6ba144a47d88","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65393040,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2026-04-01T07:29:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 07:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9266374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9266374","identity":"rs-9266374","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}