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<p>In emergency scenarios, real-time kinematic (RTK) positioning is constrained by the lack of ground infrastructure, while Precise Point Positioning (PPP) performance is often degraded in obstructed environments. To overcome these limitations, we propose a cooperative positioning framework that integrates PPP and RTK. In this approach, a UAV serves as a moving base station, determining its position via PPP, while a ground vehicle (GV) performs relative RTK positioning with respect to the UAV, yielding a moving-baseline RTK (MBRTK) solution. An improved k-index stochastic model and an iterative outlier detection strategy are incorporated to enhance robustness under challenging conditions. Experimental results show that GV-based PPP is prone to re-convergence issues due to the limited availability of high-quality observations in urban environments when relying on real-time products from the Galileo HAS service. In contrast, UAV-based PPP achieves better positioning accuracy despite shorter observation periods and lower-grade equipment, benefitting from superior satellite geometry. The average RMS errors of the UAV reach 0.253&#xa0;m/0.360&#xa0;m/0.444&#xa0;m for real-time solution and 0.096&#xa0;m/0.159&#xa0;m/0.201&#xa0;m for near real-time solution in the north, east, and up directions, respectively. Once the cooperative MBRTK framework is established, two key advantages are observed: (1) it mitigates re-convergence issues encountered in standalone GV PPP; and (2) it improves post-convergence positioning accuracy, with enhancements of 76.92% in the east and 61.17% in the up direction compared to GV-only PPP. Moreover, under a simulated 25-s correction delay, horizontal accuracy degrades by only 0.01&#xa0;m and vertical accuracy by 0.08&#xa0;m, demonstrating the robustness of the proposed MBRTK method under delayed correction scenarios.</p>

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A real-time moving baseline RTK framework for cooperative positioning of UAVs and ground vehicles

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Jia Song,
  • Lewen Zhao,
  • Wei Xu

摘要

In emergency scenarios, real-time kinematic (RTK) positioning is constrained by the lack of ground infrastructure, while Precise Point Positioning (PPP) performance is often degraded in obstructed environments. To overcome these limitations, we propose a cooperative positioning framework that integrates PPP and RTK. In this approach, a UAV serves as a moving base station, determining its position via PPP, while a ground vehicle (GV) performs relative RTK positioning with respect to the UAV, yielding a moving-baseline RTK (MBRTK) solution. An improved k-index stochastic model and an iterative outlier detection strategy are incorporated to enhance robustness under challenging conditions. Experimental results show that GV-based PPP is prone to re-convergence issues due to the limited availability of high-quality observations in urban environments when relying on real-time products from the Galileo HAS service. In contrast, UAV-based PPP achieves better positioning accuracy despite shorter observation periods and lower-grade equipment, benefitting from superior satellite geometry. The average RMS errors of the UAV reach 0.253 m/0.360 m/0.444 m for real-time solution and 0.096 m/0.159 m/0.201 m for near real-time solution in the north, east, and up directions, respectively. Once the cooperative MBRTK framework is established, two key advantages are observed: (1) it mitigates re-convergence issues encountered in standalone GV PPP; and (2) it improves post-convergence positioning accuracy, with enhancements of 76.92% in the east and 61.17% in the up direction compared to GV-only PPP. Moreover, under a simulated 25-s correction delay, horizontal accuracy degrades by only 0.01 m and vertical accuracy by 0.08 m, demonstrating the robustness of the proposed MBRTK method under delayed correction scenarios.