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<p>This paper presents a comprehensive comparative analysis of Global Navigation Satellite System (GNSS) module performance in both static and dynamic Unmanned Aerial Vehicle (UAV) environments, focusing on four major satellite navigation systems: GPS, BeiDou, NavIC, and Galileo. Two separate test scenarios is conducted here. In the dynamic test, the M8N GPS and 7Semi L89-based BeiDou modules is simultaneously mounted on a drone and evaluated using predefined waypoints in Mission Planner software. Results showed that the GPS module exhibited more stable and responsive roll, pitch, and yaw tracking during high-speed maneuvers and BeiDou module provides superior horizontal positioning accuracy due to its utilization of multiple orbital planes (MEO, GEO, IGSO). In the static evaluation, NEO M8 (GPS), PX1125S-01D (NavIC), and 7Semi L89-based Galileo modules were assessed. The NavIC-enabled PX1125S-01D demonstrated the highest positioning accuracy and the most consistent signal strength in an open-field setup in the Indian region. Galileo offeres low DOP values and excellent accuracy in multi-satellite environments, while GPS maintained reliable performance with broader global coverage. To address the limitation of short-duration trials, an extended 3-h static experiment was carried out, which confirmed NavIC’s superior stability and further emphasized the effect of long-term observation on evaluating constellation reliability. Key performance metrics such as HDOP, VDOP, GDOP, CEP, and 3DRMS are measured for assessment. The findings indicate that no single system is optimal for all use cases: NavIC and BeiDou excel in precise navigation under favorable signal conditions, while GPS provides dependable performance in fast-changing and globally diverse environments. A hybrid GNSS configuration that integrates the strengths of multiple systems could significantly enhance UAV navigation accuracy and stability across varying operational contexts.</p>

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Comparative analysis of GNSS module performance in static and UAV-based dynamic environments

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Raj Hakani,
  • Abhishek Rawat,
  • Mitchell Prajapati

摘要

This paper presents a comprehensive comparative analysis of Global Navigation Satellite System (GNSS) module performance in both static and dynamic Unmanned Aerial Vehicle (UAV) environments, focusing on four major satellite navigation systems: GPS, BeiDou, NavIC, and Galileo. Two separate test scenarios is conducted here. In the dynamic test, the M8N GPS and 7Semi L89-based BeiDou modules is simultaneously mounted on a drone and evaluated using predefined waypoints in Mission Planner software. Results showed that the GPS module exhibited more stable and responsive roll, pitch, and yaw tracking during high-speed maneuvers and BeiDou module provides superior horizontal positioning accuracy due to its utilization of multiple orbital planes (MEO, GEO, IGSO). In the static evaluation, NEO M8 (GPS), PX1125S-01D (NavIC), and 7Semi L89-based Galileo modules were assessed. The NavIC-enabled PX1125S-01D demonstrated the highest positioning accuracy and the most consistent signal strength in an open-field setup in the Indian region. Galileo offeres low DOP values and excellent accuracy in multi-satellite environments, while GPS maintained reliable performance with broader global coverage. To address the limitation of short-duration trials, an extended 3-h static experiment was carried out, which confirmed NavIC’s superior stability and further emphasized the effect of long-term observation on evaluating constellation reliability. Key performance metrics such as HDOP, VDOP, GDOP, CEP, and 3DRMS are measured for assessment. The findings indicate that no single system is optimal for all use cases: NavIC and BeiDou excel in precise navigation under favorable signal conditions, while GPS provides dependable performance in fast-changing and globally diverse environments. A hybrid GNSS configuration that integrates the strengths of multiple systems could significantly enhance UAV navigation accuracy and stability across varying operational contexts.