GPS_BeiDou Hybrid Operation Performance in Nigeria Towards Seamless Applications

GPS_BeiDou Hybrid Operation Performance in Nigeria Towards Seamless Applications | 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 GPS_BeiDou Hybrid Operation Performance in Nigeria Towards Seamless Applications Adebayo Babatunde Benedict, Adewumi Adebayo Segun, Ogobor Anthony Efua, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7982721/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 This study evaluates the performance of GPS-only, Beidou-only, and hybridized GPS_BeiDou constellations in Nigeria, aiming to assess positional stability and the benefits of multi-constellation integration. A dual-frequency u-blox ZED-F9P GNSS receiver was deployed at Ladoke GNSS Laboratory (LGL) (8.17054400°N, 4.26892783°E, 403.411m) Department of Pure and Applied Physics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, to collect continuous 24-hour GNSS observations data. The raw data collected were analyzed in four stages, including carrier-to-noise density ratio (C/N₀), number of satellites visible (NSV), satellite elevation angles, positional variation, and Single Point Positioning (SPP) accuracy. The results showed that GPS L1 signals had the highest average C/N₀ values, while BeiDou L1 signals exhibited the lowest and most variable C/N₀ values. GPS-only solutions exhibited moderate stability, while BeiDou-only results were more unstable and less accurate. The hybridized GPS_BeiDou mode of operations delivered the best positioning performance, with average values of 2DRMS of 2.9 m, CEP of 1.3 m, SEP of 3.5 m, and MRSE of 4.1 m. In conclusion, hybridizing GPS and BeiDou constellations significantly improves GNSS positioning accuracy, reliability, and availability in Nigeria. GPS provides broader coverage and more stable geometry, while BeiDou offers stronger signals and extended visibility hours. Their complementary features make the hybridized mode of operation more robust for precision applications in Nigeria. It is recommended that GNSS users in Nigeria should adopt multi-constellation hybridization and elevation masks above 20°. GPS_Beidou satellites Hybrid Operation position stability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Global Navigation Satellite Systems (GNSS) are satellite-based frameworks that provide positioning, navigation, and timing (PNT) services across the globe. These systems have revolutionized how location and movement are tracked, replacing traditional, less accurate methods with real-time, continuous data essential for modern life. The most recognized of these systems is the United States’ Global Positioning System (GPS), but other full-scale constellations are now operational, including Russia’s Glonass, Europe’s Galileo, and China’s BeiDou. Table 1.1 shows the available GNSS and their relevant information. Together, these constellations enable precise geolocation services to be used across diverse sectors [ 1 – 2 ]. In Nigeria, GNSS has become increasingly valuable, especially as the country undergoes rapid urbanization and digital transformation. GNSS plays a critical role across various sectors, including transportation, agriculture, infrastructure development, and emergency response. GNSS, a crucial location service, faces challenges in signal blockage environments like urban canyons and dense foliage. Hybrid positioning systems combine multiple GNSS constellations to overcome these limitations, leveraging their strengths while mitigating weaknesses, offering global coverage and accurate accuracy [ 3 ]. GPS, a US-developed satellite-based navigation system, offers global location accuracy through Medium Earth Orbit satellites. It's used in transportation, agriculture, telecommunications, and disaster management, and its accuracy and integration with other GNSS constellations have improved over time [ 4 ]. China's BeiDou satellite navigation system, operational since 2020, provides accurate positioning and communication services in the Asia-Pacific region. It complements other GNSS systems, improving accuracy and reliability. Hybrid systems, combining signals from GPS, BeiDou, Galileo, and GLONASS, are crucial for high-precision applications like autonomous vehicles and emergency services [ 5 ]. GNSS systems like GPS and BeiDou are crucial for various sectors, but in Nigeria, they face challenges like urban density, signal blockage, atmospheric disturbances, and limited satellite visibility, affecting their accuracy, coverage, and reliability [ 6 ]. Nigeria's urbanization and digital transformation increase demand for satellite navigation services. However, standalone GNSS systems' limitations limit performance. GPS and BeiDou use different frequencies, with GPS broadcasting on L1 (1575.42 MHz), L2 (1227.6 MHz), and L5 (1176 MHz), while BeiDou transmits on B1 (1561.098 MHz), B2 (1207.140 MHz), and B3 (1268.520 MHz). Although the L1 and B1 frequencies are close, they are distinct signals, and the two systems use different frequency sets overall. BeiDou's global presence and unique constellation offer complementary advantages, but research on optimizing hybrid operation is limited specifically, in the Nigerian context [ 7 – 8 ]. This study explores the potential of hybridizing GPS and BeiDou satellite signals in Nigeria to enhance positioning, navigation, and timing services, addressing signal degradation, coverage gaps, and accuracy limitations. Table 1 Comparison of some of the major GNSS Constellation System Launch Year Satellite Orbit Altitude GPS 1978 ~ 31 ~ 20,200 km GLONASS 1982 24 ~ 19,100 km Galileo 2011 24–30 ~ 23,200 km BeiDou 2000 35 MEO/GEO/IGSO 2. Significance of the Study in Nigeria Nigeria, located near both the geographic and geomagnetic equator within a low-latitude region has a strong interest in leveraging new Position Navigation and Timing (PNT) technologies for critical sectors like aviation and non-aviation applications including agriculture, logistics, and infrastructure development. Understanding the performance of these systems is a step toward adopting them. Therefore, a study on the GPS-BeiDou visibility, availability, and position solution performance over Nigeria is crucial for developing robust satellite navigation services by assessing how these systems perform locally, enabling precise navigation in a country with growing infrastructural needs. Figure 1 shows the location where the experiment was performed. This paper revealed how the new GNSS technologies can improve accuracy, reliability and safety for various applications from aviation to general positioning. This report will enhance Nigerian’s ability to leverage these free civilian controlled systems for economic development and critical infrastructure. Hence, this paper presents the findings on GPS_BeiDou hybrid mode of operation performance in Nigeria towards seamless applications through empirical approach. 3. Related Works Study conducted by [ 9 ] showed that integrating multiple GNSS constellations improves position accuracy and reduces convergence time in static PPP, using observation data from seven MGEX stations. The work of [ 10 ] shows signal interoperability and time synchronization across major GNSS constellations, emphasizing the importance of signal design and compatibility for common algorithms and frequencies, but not for real-world performance differences. Study on GNSS satellite visibility anomalies and regional system support, focusing on satellite geometry and atmospheric conditions was carried out by [ 11 ], but did not explore regional system integration. An improvement on RTK performance in urban environments by integrating INS and LiDAR was presented in [ 12 ]. They achieved sub-decimeter-level accuracy in GNSS-challenging environments and improved ambiguity fixing rate by over 10%. The method was tested and evaluated in simulated and real-world experiments. Study on the advantages and quality of NavIC-based position solutions for secondary service areas using real-time NMEA data from NSSTC, Al Ain, NIMT, Pathum Thani, Thailand, and TUMSAT, Japan was carried out by [ 13 ]. They found that NavIC satellites placed in GEO or IGSO from high elevation angles improve navigation efficiency in constrained or degraded GNSS satellite visibility situations. study was carried out on Satellite-Based Augmentation Systems (SBAS), specifically the Indian GAGAN system by [ 14 ], to enhance missile test range GNSS accuracy. They found significant error reductions in standalone GNSS positioning by mitigating atmospheric errors. However, the study focused on a regional GNSS environment within India and did not consider hybrid or multi-constellation configurations. Study conducted by [ 15 ] on comparing GNSS precise positioning techniques using compact GNSS modules for test range applications. shows the performance of these low-cost receivers in real-world environments, but did not explore the integration of multiple constellations or hybrid positioning systems. Integrating NavIC with Galileo presented by [ 7 ] significantly improved RTK accuracy and reliability over long distances, but the study focused on a specific regional setup and did not explore potential benefits. Performance study on a regional GNSS (NavIC) in high-dynamic conditions by [ 8 ], but their study focused on India's standalone system and did not explore hybrid or multi-GNSS approaches, and the region's NavIC-optimized satellite geometry may not reflect West Africa's complex reception conditions. The performance of the low-cost GNSS module, specifically the u-blox ZED-F9P, for Precise Point Positioning in low-latitude environments was presented by [ 3 ]. The study found that the ZED-F9P can achieve horizontal positioning accuracy below 5 mm and vertical accuracy below 20 cm under favorable conditions. Comparison study of SPP, RTK, and MADOCA PPP for UAV integration was carried out by [ 15 ]. Results show SPP achieves 3D precision of 2.614 m, RTK improves it to 0.070 m, and MADOCA PPP offers superior performance. 4. Research Method For this study, GNSS Reference Coordinate (RC) was created at Ladoke GNSS Laboratory (LGL), Department of Pure and Applied Physics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. A dual-frequency u-blox ZED-F9P GNSS receiver was deployed to collect continuous 24-hour GNSS observations at a 4 Hz sampling rate over a two-week period in May 2025.The experimental set up is as shown in figure (2). The raw data were converted to RINEX 3.03 format using RTKLIB tools and analyzed in four stages. Firstly, the carrier-to-noise density ratio (C/N₀), a measure of GNSS signal strength in decibels per hertz, was extracted and processed in MATLAB to assess diurnal variations in signal quality. Secondly, the number of satellites visible (NSV) per epoch was obtained from RTKPOST solutions, while satellite elevation angles were analyzed using Tec-suite. Thirdly, positional variation was evaluated by computing the standard deviations of latitude, longitude, and altitude over time. Finally, Single Point Positioning (SPP) accuracy was assessed using standard performance metrics: two-dimensional root mean square (2DRMS), circular error probable (CEP), spherical error probable (SEP), and mean radial spherical error (MRSE). 5. Results and Discussion Table 2 presents the results of the Number of Satellites Visible (NSV) for both GPS and BeiDou constellations over a 14-day period of observation in May 2025. It was witnessed that GPS maintains a high level of stability in its satellite visibility. The minimum number of visible GPS satellites is 6, with a consistent maximum of 11 satellites. The average NSV per day is approximately 8.23, with minor deviations. BeiDou, on the other hand, shows slightly more variability in its NSV. The maximum number of BeiDou satellites is 8, but the minimum number is 4, which is the minimum required for a 3D positional fix. The average number of BeiDou satellites visible ranges between 5.87 and 6.22. A noticeable decline in average visibility occurred between 12 and 14 May 2025, suggesting that BeiDou may not always offer the same level of coverage and reliability as GPS, especially in challenging environments or obstructed conditions. Table 3 reveled that GPS outperforms BeiDou in terms of consistency, stability, and total Pseudo-Random Noise (PRN) availability, duration, and total number of PRN available during a 14-day observation period. GPS maintained 31 unique PRNs visible each day, indicating its reliability. BeiDou, on the other hand, exhibited variability in its daily PRN counts, ranging from 28 to 31 visible satellites. The least visible satellite in each constellation consistently maintained approximately 7 hours of availability. However, BeiDou's least visible PRNs exhibited inconsistent and short durations, suggesting potential limitations in satellite geometry, regional coverage, or operational performance. The findings underscore the importance of hybrid GNSS solutions, where combining GPS and BeiDou enhances overall availability, mitigates individual system weaknesses, and ensures robust positioning capabilities in dynamic or obstructed environments. Table 2: GPS and Beidou constellations NSV Figure 3 represents the mean Carrier-to-Noise ratio (C/N₀) values for GPS and BeiDou constellations across L1 and L2 frequency bands. GPS consistently maintains higher C/N₀ values compared to BeiDou for most hours of each day, exhibiting relative stability with minor fluctuations. BeiDou L1 signals show more variability with periods of lower C/N₀, often dipping below 36 dB-Hz, especially during morning and midday hours. GPS L1 signals demonstrate a consistent advantage in signal quality, which is important for precise positioning. In contrast, Beidou's C/N₀ values on L2 often exceed those of GPS, with peaks reaching above 45 dB-Hz on some days. GPS L2 maintains a more consistent, albeit lower, C/N₀ level around 35 to 37 dB-Hz with fewer extreme variations. A general trend is observed where GPS L1 signals provide the strongest and most stable signal quality. BeiDou, while showing weaker signals on L1, demonstrates strong L2 performance with significant peaks in signal strength. Figure 4 represent the result of comparing of GPS and BeiDou in terms of positioning stability and accuracy across latitude, longitude, and altitude. GPS maintained a stable standard deviation of 1.3–1.6 m in latitude, while BeiDou showed higher variability. GPS was more robust in mitigating horizontal positioning errors, with a lower standard deviation of 1.3–1.8 m. Altitude accuracy showed the largest performance gap, with GPS consistently providing errors of 5–6 m, while BeiDou recorded values ranging from 6–9 m. GPS provided superior solution stability and reduced error spread compared to Beidou across all coordinate domains. Figure 5 depict the comparison of average satellite elevation angles over a 24-hour period for GPS and Beidou satellite systems. It was witness that Beidou consistently maintains higher average elevation angles compared to GPS, with a more stable and elevated trend. GPS average elevation fluctuates more significantly, reaching two local maxima around 09:00 UTC and 21:00 UTC, both approaching approximately 29 degrees. Beidou's elevation profile remains comparatively stable, with values generally ranging between 26 and 33 degrees. The relative stability of Beidou's performance can be attributed to its constellation design, which includes a mix of geostationary, inclined geosynchronous, and medium Earth orbit satellites. The analysis of average satellite elevation over a 24-hour period demonstrates the superior and more consistent performance of Beidou system relative to GPS, contributing to improved signal quality and positioning accuracy, particularly during hours when GPS elevation is at its lowest. hours. Figure 5: Hourly average satellite elevation angle for GPS and BeiDou constellations relative to visible Figure 6 presents a comparative analysis of position accuracy parameters metrics obtained from the three satellites constellation configurations namely; GPS only, BeiDou only, and the hybridized GPS_BeiDou mode of operation. The position accuracy parameters are 2DRMS, CEP, SEP, and MRSE, which measure horizontal and 3D positional accuracy. The hybridized GPS_BeiDou mode of operation consistently outperforms the individual satellite by delivering the lowest error values and demonstrating the benefits of multi-GNSS integration. The hybridized mode of operation's accuracy remains relatively stable over the entire observation period, highlighting the reliability and robustness of dual-constellation solutions in improving GNSS positioning accuracy. In contrast, the Beidou-only exhibits the highest error values in all parameter metrics, with 2DRMS values ranging between 5 to 10 meters. The GPS-only system shows moderate and stable performance, with error values generally lower than those of Beidou but generally higher than the hybridized GPS_BeiDou constellation. The hybridized GPS_BeiDou configuration demonstrates consistently superior positioning performance across all evaluated metrics, demonstrating a high level of horizontal positioning precision. The consistency and accuracy of the combined solution make it particularly well suited for high precision GNSS applications, such as land surveying, autonomous navigation, and other mission critical operations in Nigeria. Overall, the data presented provides compelling empirical evidence supporting the use of multi-GNSS hybridization, particularly the integration of GPS with BeiDou, in practical applications where enhanced reliability, continuity, and accuracy are essential over prolonged observation periods. 6. Conclusion In conclusion, the hybridizing GPS_BeiDou constellations significantly improves GNSS positioning accuracy, reliability, and availability in Nigeria. GPS provides broader coverage and more stable geometry, while BeiDou offers stronger signals and extended visibility hours. Their complementary features make the hybridized mode of operation more robust for precision applications in Nigeria. It is recommended that GNSS users in Nigeria should adopt multi-constellation hybridization and elevation masks above 20°. Declarations Authors Contributions: Authors’ Contributions ABB : Study conception and design, data acquisition, data analysis and interpretation, original draft writing. AAS : Study conception and design, data acquisition, data analysis and interpretation, original draft writing, review and editing. OAE: Study conception and design, data analysis and interpretation, original draft writing, review and editing. HMS : Study conception and design, data analysis and interpretation, review and editing. ANO: Study conception and design, data analysis and interpretation, original draft writing, review and editing. EDE: Study conception and design, data analysis and interpretation, review and editing. ABG: Study conception and design, data analysis and interpretation, review and editing. Funding The authors received no funding for this research. Data availability: Data is provided within the manuscript files Ethics approval: Ethics approval was not required for this study. Hence, no ethics approval declaration is available. Consent to publish Consent to publish was not required for this study. Hence, no consent to publish declaration is available. Consent to participate Consent to participate was not required for this study. Hence, no consent to participate declaration is available. Clinical trial number: Clinical trial is not applicable Competing interests The authors declare no competing interests References Adewumi, A., & Ibraheem, A. A. (2021). a Review of Global Navigation Satellite Systems (GNSS) and Its Applications. International Journal of Scientific and Engineering Research, 12, 1042-1049 Efua, A. O., Adewumi, A. S., Ayantunji, G. B., & Ogobor, E. A. (2025). Global Navigation Satellite Systems: A Key Player in Technological Advancement. In Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024). 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11:12:59","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71095,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/f82868579f8fa2d819493e9e.html"},{"id":96239449,"identity":"c474b0c3-a958-44e2-b56d-18f9256bcc35","added_by":"auto","created_at":"2025-11-19 07:06:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":654909,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical Locations of the Study Area over LAUTECH, Nigeria (8°10′18″N, 4°16′18″E)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/3077fb675b27b3e97f44deb2.png"},{"id":96239245,"identity":"09d23179-00f7-4f04-b0e5-6a6180157ac7","added_by":"auto","created_at":"2025-11-19 07:05:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220757,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Setup for the GPS_BeiDou position Solution Study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/8bfeb56c4d78352ee7b7fbd7.png"},{"id":95826091,"identity":"8370bff6-703b-41a3-a860-665f68bbbbb0","added_by":"auto","created_at":"2025-11-13 11:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264688,"visible":true,"origin":"","legend":"\u003cp\u003eGPS and BeiDou hourly diurnal mean generated from RNS shorted MATLAB script at L1 and L2 Bands\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/55584b3806f593e05f413b47.png"},{"id":96239682,"identity":"e42dd0f2-fd08-4eae-ab55-21a376920efb","added_by":"auto","created_at":"2025-11-19 07:07:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104641,"visible":true,"origin":"","legend":"\u003cp\u003eStandard deviation of positional variations in latitude, longitude, and altitude.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/9a1611eb7b097578bf2ca3f4.png"},{"id":95826073,"identity":"eb6d76a0-1e1a-4545-b37a-ce7c625b27fe","added_by":"auto","created_at":"2025-11-13 11:12:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105419,"visible":true,"origin":"","legend":"\u003cp\u003eHourly average satellite elevation angle for GPS and BeiDou constellations relative to visible\u003c/p\u003e\n\u003cp\u003ehours.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/af0cf1baa5ae62fd9f7d169a.png"},{"id":95826075,"identity":"c0acb339-5973-41ff-8c45-0b9a2d9930c8","added_by":"auto","created_at":"2025-11-13 11:12:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":145336,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Positioning Precision Parameter Metrics of GPS only, Beidou only, and GPS_Beidou\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/7300e5be5bfc43b5ff8c34d0.png"},{"id":102745510,"identity":"da267fd8-940f-40bd-8089-d63b5f61ae16","added_by":"auto","created_at":"2026-02-16 08:51:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1872690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7982721/v1/fe5db975-adaa-404e-bac7-8cb133dcb118.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GPS_BeiDou Hybrid Operation Performance in Nigeria Towards Seamless Applications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal Navigation Satellite Systems (GNSS) are satellite-based frameworks that provide positioning, navigation, and timing (PNT) services across the globe. These systems have revolutionized how location and movement are tracked, replacing traditional, less accurate methods with real-time, continuous data essential for modern life. The most recognized of these systems is the United States\u0026rsquo; Global Positioning System (GPS), but other full-scale constellations are now operational, including Russia\u0026rsquo;s Glonass, Europe\u0026rsquo;s Galileo, and China\u0026rsquo;s BeiDou. Table\u0026nbsp;1.1 shows the available GNSS and their relevant information. Together, these constellations enable precise geolocation services to be used across diverse sectors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Nigeria, GNSS has become increasingly valuable, especially as the country undergoes rapid urbanization and digital transformation. GNSS plays a critical role across various sectors, including transportation, agriculture, infrastructure development, and emergency response. GNSS, a crucial location service, faces challenges in signal blockage environments like urban canyons and dense foliage. Hybrid positioning systems combine multiple GNSS constellations to overcome these limitations, leveraging their strengths while mitigating weaknesses, offering global coverage and accurate accuracy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. GPS, a US-developed satellite-based navigation system, offers global location accuracy through Medium Earth Orbit satellites. It's used in transportation, agriculture, telecommunications, and disaster management, and its accuracy and integration with other GNSS constellations have improved over time [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. China's BeiDou satellite navigation system, operational since 2020, provides accurate positioning and communication services in the Asia-Pacific region. It complements other GNSS systems, improving accuracy and reliability. Hybrid systems, combining signals from GPS, BeiDou, Galileo, and GLONASS, are crucial for high-precision applications like autonomous vehicles and emergency services [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. GNSS systems like GPS and BeiDou are crucial for various sectors, but in Nigeria, they face challenges like urban density, signal blockage, atmospheric disturbances, and limited satellite visibility, affecting their accuracy, coverage, and reliability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nigeria's urbanization and digital transformation increase demand for satellite navigation services. However, standalone GNSS systems' limitations limit performance. GPS and BeiDou use different frequencies, with GPS broadcasting on L1 (1575.42 MHz), L2 (1227.6 MHz), and L5 (1176 MHz), while BeiDou transmits on B1 (1561.098 MHz), B2 (1207.140 MHz), and B3 (1268.520 MHz). Although the L1 and B1 frequencies are close, they are distinct signals, and the two systems use different frequency sets overall. BeiDou's global presence and unique constellation offer complementary advantages, but research on optimizing hybrid operation is limited specifically, in the Nigerian context [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study explores the potential of hybridizing GPS and BeiDou satellite signals in Nigeria to enhance positioning, navigation, and timing services, addressing signal degradation, coverage gaps, and accuracy limitations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of some of the major GNSS Constellation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaunch Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSatellite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrbit Altitude\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e~\u0026thinsp;20,200 km\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLONASS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e~\u0026thinsp;19,100 km\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGalileo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e~\u0026thinsp;23,200 km\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeiDou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMEO/GEO/IGSO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Significance of the Study in Nigeria","content":"\u003cp\u003eNigeria, located near both the geographic and geomagnetic equator within a low-latitude region has a strong interest in leveraging new Position Navigation and Timing (PNT) technologies for critical sectors like aviation and non-aviation applications including agriculture, logistics, and infrastructure development. Understanding the performance of these systems is a step toward adopting them. Therefore, a study on the GPS-BeiDou visibility, availability, and position solution performance over Nigeria is crucial for developing robust satellite navigation services by assessing how these systems perform locally, enabling precise navigation in a country with growing infrastructural needs. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location where the experiment was performed. This paper revealed how the new GNSS technologies can improve accuracy, reliability and safety for various applications from aviation to general positioning. This report will enhance Nigerian\u0026rsquo;s ability to leverage these free civilian controlled systems for economic development and critical infrastructure. Hence, this paper presents the findings on GPS_BeiDou hybrid mode of operation performance in Nigeria towards seamless applications through empirical approach.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Related Works","content":"\u003cp\u003eStudy conducted by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] showed that integrating multiple GNSS constellations improves position accuracy and reduces convergence time in static PPP, using observation data from seven MGEX stations. The work of [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] shows signal interoperability and time synchronization across major GNSS constellations, emphasizing the importance of signal design and compatibility for common algorithms and frequencies, but not for real-world performance differences. Study on GNSS satellite visibility anomalies and regional system support, focusing on satellite geometry and atmospheric conditions was carried out by [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], but did not explore regional system integration. An improvement on RTK performance in urban environments by integrating INS and LiDAR was presented in [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They achieved sub-decimeter-level accuracy in GNSS-challenging environments and improved ambiguity fixing rate by over 10%. The method was tested and evaluated in simulated and real-world experiments. Study on the advantages and quality of NavIC-based position solutions for secondary service areas using real-time NMEA data from NSSTC, Al Ain, NIMT, Pathum Thani, Thailand, and TUMSAT, Japan was carried out by [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They found that NavIC satellites placed in GEO or IGSO from high elevation angles improve navigation efficiency in constrained or degraded GNSS satellite visibility situations. study was carried out on Satellite-Based Augmentation Systems (SBAS), specifically the Indian GAGAN system by [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], to enhance missile test range GNSS accuracy. They found significant error reductions in standalone GNSS positioning by mitigating atmospheric errors. However, the study focused on a regional GNSS environment within India and did not consider hybrid or multi-constellation configurations. Study conducted by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] on comparing GNSS precise positioning techniques using compact GNSS modules for test range applications. shows the performance of these low-cost receivers in real-world environments, but did not explore the integration of multiple constellations or hybrid positioning systems. Integrating NavIC with Galileo presented by [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] significantly improved RTK accuracy and reliability over long distances, but the study focused on a specific regional setup and did not explore potential benefits. Performance study on a regional GNSS (NavIC) in high-dynamic conditions by [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but their study focused on India's standalone system and did not explore hybrid or multi-GNSS approaches, and the region's NavIC-optimized satellite geometry may not reflect West Africa's complex reception conditions. The performance of the low-cost GNSS module, specifically the u-blox ZED-F9P, for Precise Point Positioning in low-latitude environments was presented by [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The study found that the ZED-F9P can achieve horizontal positioning accuracy below 5 mm and vertical accuracy below 20 cm under favorable conditions. Comparison study of SPP, RTK, and MADOCA PPP for UAV integration was carried out by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Results show SPP achieves 3D precision of 2.614 m, RTK improves it to 0.070 m, and MADOCA PPP offers superior performance.\u003c/p\u003e"},{"header":"4. Research Method","content":"\u003cp\u003eFor this study, GNSS Reference Coordinate (RC) was created at Ladoke GNSS Laboratory (LGL), Department of Pure and Applied Physics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. A dual-frequency u-blox ZED-F9P GNSS receiver was deployed to collect continuous 24-hour GNSS observations at a 4 Hz sampling rate over a two-week period in May 2025.The experimental set up is as shown in figure (2). The raw data were converted to RINEX 3.03 format using RTKLIB tools and analyzed in four stages. Firstly, the carrier-to-noise density ratio (C/N₀), a measure of GNSS signal strength in decibels per hertz, was extracted and processed in MATLAB to assess diurnal variations in signal quality. Secondly, the number of satellites visible (NSV) per epoch was obtained from RTKPOST solutions, while satellite elevation angles were analyzed using Tec-suite. Thirdly, positional variation was evaluated by computing the standard deviations of latitude, longitude, and altitude over time. Finally, Single Point Positioning (SPP) accuracy was assessed using standard performance metrics: two-dimensional root mean square (2DRMS), circular error probable (CEP), spherical error probable (SEP), and mean radial spherical error (MRSE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. Results and Discussion","content":"\u003cp\u003eTable 2 presents the results of the Number of Satellites Visible (NSV) for both GPS and BeiDou constellations over a 14-day period of observation in May 2025. It was witnessed that GPS maintains a high level of stability in its satellite visibility. The minimum number of visible GPS satellites is 6, with a consistent maximum of 11 satellites. The average NSV per day is approximately 8.23, with minor deviations. BeiDou, on the other hand, shows slightly more variability in its NSV. The maximum number of BeiDou satellites is 8, but the minimum number is 4, which is the minimum required for a 3D positional fix. The average number of BeiDou satellites visible ranges between 5.87 and 6.22. A noticeable decline in average visibility occurred between 12 and 14 May 2025, suggesting that BeiDou may not always offer the same level of coverage and reliability as GPS, especially in challenging environments or obstructed conditions. \u0026nbsp;Table 3 reveled that GPS outperforms BeiDou in terms of consistency, stability, and total Pseudo-Random Noise (PRN) availability, duration, and total number of PRN available during a 14-day observation period. GPS maintained 31 unique PRNs visible each day, indicating its reliability. BeiDou, on the other hand, exhibited variability in its daily PRN counts, ranging from 28 to 31 visible satellites. The least visible satellite in each constellation consistently maintained approximately 7 hours of availability. However, BeiDou\u0026apos;s least visible PRNs exhibited inconsistent and short durations, suggesting potential limitations in satellite geometry, regional coverage, or operational performance. The findings underscore the importance of hybrid GNSS solutions, where combining GPS and BeiDou enhances overall availability, mitigates individual system weaknesses, and ensures robust positioning capabilities in dynamic or obstructed environments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2: GPS and Beidou constellations NSV\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e represents the mean Carrier-to-Noise ratio (C/N₀) values for GPS and BeiDou constellations across L1 and L2 frequency bands. GPS consistently maintains higher C/N₀ values compared to BeiDou for most hours of each day, exhibiting relative stability with minor fluctuations. BeiDou L1 signals show more variability with periods of lower C/N₀, often dipping below 36 dB-Hz, especially during morning and midday hours. GPS L1 signals demonstrate a consistent advantage in signal quality, which is important for precise positioning. In contrast, Beidou\u0026apos;s C/N₀ values on L2 often exceed those of GPS, with peaks reaching above 45 dB-Hz on some days. GPS L2 maintains a more consistent, albeit lower, C/N₀ level around 35 to 37 dB-Hz with fewer extreme variations. A general trend is observed where GPS L1 signals provide the strongest and most stable signal quality. BeiDou, while showing weaker signals on L1, demonstrates strong L2 performance with significant peaks in signal strength.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e represent the result of comparing of GPS and BeiDou in terms of positioning stability and accuracy across latitude, longitude, and altitude. GPS maintained a stable standard deviation of 1.3\u0026ndash;1.6 m in latitude, while BeiDou showed higher variability. GPS was more robust in mitigating horizontal positioning errors, with a lower standard deviation of 1.3\u0026ndash;1.8 m. Altitude accuracy showed the largest performance gap, with GPS consistently providing errors of 5\u0026ndash;6 m, while BeiDou recorded values ranging from 6\u0026ndash;9 m. GPS provided superior solution stability and reduced error spread compared to Beidou across all coordinate domains.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 depict the comparison of average satellite elevation angles over a 24-hour period for GPS and Beidou satellite systems. It was witness that Beidou consistently maintains higher average elevation angles compared to GPS, with a more stable and elevated trend. GPS average elevation fluctuates more significantly, reaching two local maxima around 09:00 UTC and 21:00 UTC, both approaching approximately 29 degrees. Beidou\u0026apos;s elevation profile remains comparatively stable, with values generally ranging between 26 and 33 degrees. The relative stability of Beidou\u0026apos;s performance can be attributed to its constellation design, which includes a mix of geostationary, inclined geosynchronous, and medium Earth orbit satellites. The analysis of average satellite elevation over a 24-hour period demonstrates the superior and more consistent performance of Beidou system relative to GPS, contributing to improved signal quality and positioning accuracy, particularly during hours when GPS elevation is at its lowest.\u003c/p\u003e\n\u003cp\u003ehours.\u003c/p\u003e\n\u003cp\u003eFigure 5: Hourly average satellite elevation angle for GPS and BeiDou constellations relative to visible\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents a comparative analysis of position accuracy parameters metrics obtained from the three satellites constellation configurations namely; GPS only, BeiDou only, and the hybridized GPS_BeiDou mode of operation. The position accuracy parameters are 2DRMS, CEP, SEP, and MRSE, which measure horizontal and 3D positional accuracy. The hybridized GPS_BeiDou mode of operation consistently outperforms the individual satellite by delivering the lowest error values and demonstrating the benefits of multi-GNSS integration. The hybridized mode of operation\u0026apos;s accuracy remains relatively stable over the entire observation period, highlighting the reliability and robustness of dual-constellation solutions in improving GNSS positioning accuracy. In contrast, the Beidou-only exhibits the highest error values in all parameter metrics, with 2DRMS values ranging between 5 to 10 meters. The GPS-only system shows moderate and stable performance, with error values generally lower than those of Beidou but generally higher than the hybridized GPS_BeiDou constellation. The hybridized GPS_BeiDou configuration demonstrates consistently superior positioning performance across all evaluated metrics, demonstrating a high level of horizontal positioning precision. The consistency and accuracy of the combined solution make it particularly well suited for high precision GNSS applications, such as land surveying, autonomous navigation, and other mission critical operations in Nigeria. Overall, the data presented provides compelling empirical evidence supporting the use of multi-GNSS hybridization, particularly the integration of GPS with BeiDou, in practical applications where enhanced reliability, continuity, and accuracy are essential over prolonged observation periods.\u003c/p\u003e\n"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, the hybridizing GPS_BeiDou constellations significantly improves GNSS positioning accuracy, reliability, and availability in Nigeria. GPS provides broader coverage and more stable geometry, while BeiDou offers stronger signals and extended visibility hours. Their complementary features make the hybridized mode of operation more robust for precision applications in Nigeria. It is recommended that GNSS users in Nigeria should adopt multi-constellation hybridization and elevation masks above 20\u0026deg;.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; Contributions \u003cstrong\u003eABB\u003c/strong\u003e: Study conception and design, data acquisition, data analysis and interpretation, original draft writing. \u003cstrong\u003eAAS\u003c/strong\u003e: Study conception and design, data acquisition, data analysis and interpretation, original draft writing, review and editing. \u003cstrong\u003eOAE:\u003c/strong\u003e Study conception and design, data analysis and interpretation, original draft writing, review and editing. \u003cstrong\u003eHMS\u003c/strong\u003e: Study conception and design, data analysis and interpretation, review and editing. \u003cstrong\u003eANO:\u003c/strong\u003e Study conception and design, data analysis and interpretation, original draft writing, review and editing. \u003cstrong\u003eEDE:\u003c/strong\u003e Study conception and design, data analysis and interpretation, review and editing. \u003cstrong\u003eABG:\u003c/strong\u003e Study conception and design, data analysis and interpretation, review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript files \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this study. Hence, no ethics approval declaration is available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish was not required for this study. Hence, no consent to publish declaration is available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to participate was not required for this study. Hence, no consent to participate declaration is available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial is not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdewumi, A., \u0026amp; Ibraheem, A. A. (2021). a Review of Global Navigation Satellite Systems (GNSS) and Its Applications. International Journal of Scientific and Engineering Research, 12, 1042-1049\u003c/li\u003e\n\u003cli\u003eEfua, A. O., Adewumi, A. S., Ayantunji, G. B., \u0026amp; Ogobor, E. A. (2025). Global Navigation Satellite Systems: A Key Player in Technological Advancement. In Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024). Atlantis Press. https://doi.org/ 10.299 1/978-94-6463-644-4_16\u003c/li\u003e\n\u003cli\u003eAdewumi, A. S., \u0026Agrave;l\u0026agrave;gb\u0026eacute;, G. A., Azeez, I. A., Ayantunji, G. B., Ogobor, E. A., Oyelowo, O., Fajobi, I., \u0026amp; Eleyele, D. E. (2025). PPP potential of a compact, low-cost GNSS receiver\u0026rsquo;s module in a low latitude region: A case study at the Lautech GNSS Laboratory, Nigeria. In I. Adimula et al. (Eds.), Proceedings of the 8th URSI-NG Annual Conference (URSI-NG2024) (pp. 175\u0026ndash;180). Atlantis Press. https://doi.org/10.2991/978-94-6463-644-4_17\u003c/li\u003e\n\u003cli\u003ePillai, A. S., Mahato, S., Goswami, M., Banerjee, P., \u0026amp; Bose, A. (2023). Comparison of GNSS precise positioning techniques using compact GNSS modules for test range applications. In 3rd International Conference on Range Technology, Chandipur (pp. XXX\u0026ndash;XXX). IEEE. https://doi.org/10.1109/ICORT56052.2023.10249186 (Note: Please add pages if available)\u003c/li\u003e\n\u003cli\u003eMahato S., S. Sarkar, M. Goswami, S. Kundu and A. Bose (2023), \u0026ldquo;GLONASS-NavIC hybrid operation from India towards seamless and improved performance,\u0026rdquo; National Academy Science Letters, vol. 46, pp. 245-250, 2023. DOI: 10.1007/s40009-023-01232-z\u003c/li\u003e\n\u003cli\u003eDutta D., Mahato S., S. Dan, A. Santra, S. Dey and A. Bose, \u0026ldquo;Galileo-NavIC hybrid operation towards improved performance and user benefits,\u0026rdquo; Journal of the Indian Society of Remote Sensing, vol. 51, no. 4, pp. 757\u0026ndash;769, 2023. DOI: 10.1007/s12524-022-01660-2\u003c/li\u003e\n\u003cli\u003eMahato, S., Kumar, V., Singh, A., Deshpande, D., Anjan, A., \u0026amp; Mitra, A. K. (2024). NavIC-Galileo long distance RTK positioning performance. In 2024 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON) (pp. 205\u0026ndash;208). IEEE. https://doi.org./10. 1109/EDKCON62339.2024.10870784\u003c/li\u003e\n\u003cli\u003eGoswami, M., Mahato, S., Dan, S., Ghatak, R., \u0026amp; Bose, A. (2025). Performance evaluation of standalone NavIC for the Indian missile program. Defence Science Journal, 75(5), 637\u0026ndash;644. https://doi.org/10.14429/dsj.19755\u003c/li\u003e\n\u003cli\u003eMalik, M. (2020). Statistical assessment of static precise point positioning using GNSS multi-constellation. 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Shen (2023) \u0026quot;Enhancing RTK Performance in Urban Environments by Tightly Integrating INS and LiDAR,\u0026quot; in IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 9845-9856, Aug., doi: 10.1109/TVT.2023 .3257874.\u003c/li\u003e\n\u003cli\u003eSantra, A., Mahato, S., Kundu, S. \u003cem\u003eet al.\u003c/em\u003e NavIC Positioning from the Secondary Service Region and Beyond: A Study Using Compact NavIC Modules. \u003cem\u003eProc. Natl. Acad. Sci., India, Sect. A Phys. Sci.\u003c/em\u003e\u003cstrong\u003e93\u003c/strong\u003e, 565\u0026ndash;572 (2023). https://doi.org/10.1007/s40010-023-00825-z\u003c/li\u003e\n\u003cli\u003eGoswami, M., Mahato, S., Ghatak, R. et al. Potential of Satellite-Based Augmentation Systems (SBAS) in Test and Evaluation of Missiles in Indian Test Range Applications. J Indian Soc Remote Sens 51, 2537\u0026ndash;2547 (2023). https://doi.org/10.1007/s12524-023-01787-w\u003c/li\u003e\n\u003cli\u003ePillai A. S., S. Mahato, G. N. Kar, M. Goswami, B. Panda and S. Kundu (2025) \u0026quot;Performance Evaluation of MADOCA PPP with Existing Positioning Techniques for UAV Altitude Determination,\u0026quot; 2025 International Conference on Computing, Intelligence, and Application (CIACON), Durgapur, India, 2025, pp. 1-5, doi:10.1109/CIACON65 473.20 25.11189728\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"GPS_Beidou, satellites, Hybrid, Operation, position, stability","lastPublishedDoi":"10.21203/rs.3.rs-7982721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7982721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the performance of GPS-only, Beidou-only, and hybridized GPS_BeiDou constellations in Nigeria, aiming to assess positional stability and the benefits of multi-constellation integration. A dual-frequency u-blox ZED-F9P GNSS receiver was deployed at Ladoke GNSS Laboratory (LGL) (8.17054400\u0026deg;N, 4.26892783\u0026deg;E, 403.411m) Department of Pure and Applied Physics, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, to collect continuous 24-hour GNSS observations data. The raw data collected were analyzed in four stages, including carrier-to-noise density ratio (C/N₀), number of satellites visible (NSV), satellite elevation angles, positional variation, and Single Point Positioning (SPP) accuracy. The results showed that GPS L1 signals had the highest average C/N₀ values, while BeiDou L1 signals exhibited the lowest and most variable C/N₀ values. GPS-only solutions exhibited moderate stability, while BeiDou-only results were more unstable and less accurate. The hybridized GPS_BeiDou mode of operations delivered the best positioning performance, with average values of 2DRMS of 2.9 m, CEP of 1.3 m, SEP of 3.5 m, and MRSE of 4.1 m. In conclusion, hybridizing GPS and BeiDou constellations significantly improves GNSS positioning accuracy, reliability, and availability in Nigeria. GPS provides broader coverage and more stable geometry, while BeiDou offers stronger signals and extended visibility hours. Their complementary features make the hybridized mode of operation more robust for precision applications in Nigeria. It is recommended that GNSS users in Nigeria should adopt multi-constellation hybridization and elevation masks above 20\u0026deg;.\u003c/p\u003e","manuscriptTitle":"GPS_BeiDou Hybrid Operation Performance in Nigeria Towards Seamless Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:12:54","doi":"10.21203/rs.3.rs-7982721/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":"6726e787-b7b6-45b1-a027-99d9c3ad33c4","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:57:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 11:12:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7982721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7982721","identity":"rs-7982721","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}