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<p>In recent years, network real-time kinematic (RTK) technology has become one of the most widely employed techniques for high-precision Global Navigation Satellite System (GNSS) real-time positioning. The core technology of network RTK involves modeling regional ionospheric errors to mitigate the effects of increasing baseline distances. However, research indicates that these methods are still limited in their effectiveness, particularly as baseline distances increase and during periods of high ionospheric activity. Existing studies suggest that addressing these limitations may be possible through the calculation of ionospheric uncertainty. Nevertheless, the accuracy of uncertainty information during the time periods of ionospheric scintillation has not been thoroughly investigated. This study focuses on the application scenario of long-baseline network RTK and examines the accuracy and the behavior of uncertainty information derived from a network with average baseline distances of 150km during ionospheric scintillation and quiet periods. The research finds that ionospheric scintillation tends to lead to inaccurate uncertainty estimation, and often underestimating actual errors. Based on these findings, this study proposes new methods in which users receive ionospheric scintillation information from the server to optimize their positioning strategy, aimed at improving RTK positioning results. The results demonstrate that the proposed method can significantly enhance performance, with positioning errors reduced by approximately 50% during the most effective periods. Meanwhile, comparative testing revealed that the overall performance shows notable improvement compared to traditional methods.</p>

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New data processing methods for network RTK positioning: improving ionospheric uncertainty management during ionospheric scintillations

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Wenhao Zhang,
  • Jinsheng Zhang,
  • Xiang Zuo,
  • Haojun Li,
  • Guofu Pan,
  • Jinling Wang

摘要

In recent years, network real-time kinematic (RTK) technology has become one of the most widely employed techniques for high-precision Global Navigation Satellite System (GNSS) real-time positioning. The core technology of network RTK involves modeling regional ionospheric errors to mitigate the effects of increasing baseline distances. However, research indicates that these methods are still limited in their effectiveness, particularly as baseline distances increase and during periods of high ionospheric activity. Existing studies suggest that addressing these limitations may be possible through the calculation of ionospheric uncertainty. Nevertheless, the accuracy of uncertainty information during the time periods of ionospheric scintillation has not been thoroughly investigated. This study focuses on the application scenario of long-baseline network RTK and examines the accuracy and the behavior of uncertainty information derived from a network with average baseline distances of 150km during ionospheric scintillation and quiet periods. The research finds that ionospheric scintillation tends to lead to inaccurate uncertainty estimation, and often underestimating actual errors. Based on these findings, this study proposes new methods in which users receive ionospheric scintillation information from the server to optimize their positioning strategy, aimed at improving RTK positioning results. The results demonstrate that the proposed method can significantly enhance performance, with positioning errors reduced by approximately 50% during the most effective periods. Meanwhile, comparative testing revealed that the overall performance shows notable improvement compared to traditional methods.