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Long-term ionospheric anomaly monitoring for ground based augmentation systems

机译:地面增强系统的长期电离层异常监测

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摘要

Extreme ionospheric anomalies can pose a potential integrity threat to ground-based augmentation of the Global Positioning System (GPS), and thus the development of ionospheric anomaly threat models for each region of operation is essential for system design and operation. This paper presents a methodology for automated long-term ionospheric anomaly monitoring, which will be used to build an ionospheric anomaly threat model, evaluate its validity over the life cycle of the system, continuously monitor ionospheric anomalies, and update the threat model if necessary. This procedure automatically processes GPS data collected from external networks and estimates ionospheric gradients at regular intervals. If ionospheric gradients large enough to be potentially hazardous to users are identified, manual data examination is triggered. This paper also develops a simplified truth processing method to create precise ionospheric delay estimates in near real-time, which is the key to automating the ionospheric monitoring procedure. The performance of the method is examined using data from the 20 November 2003 and 9 November 2004 ionospheric storms. These results demonstrate the effectiveness of simplified truth processing within long-term ionosphere monitoring. From the case studies, the automated procedure successfully identified extreme ionospheric anomalies, including the two worst ionospheric gradients observed and validated previously based on manual analysis. The automation of data processing enables us to analyze ionospheric data continuously going forward and to more accurately categorize ionospheric behavior under both nominal and anomalous conditions.
机译:极端的电离层异常可能会对全球定位系统(GPS)的地面扩展构成潜在的完整性威胁,因此针对每个操作区域开发电离层异常威胁模型对于系统设计和运行至关重要。本文介绍了一种自动长期电离层异常监测的方法,该方法将用于建立电离层异常威胁模型,评估其在系统生命周期内的有效性,连续监测电离层异常并在必要时更新威胁模型。此过程将自动处理从外部网络收集的GPS数据,并以固定间隔估算电离层梯度。如果识别出的电离层梯度足够大,可能会对用户造成危险,则将触发手动数据检查。本文还开发了一种简化的真值处理方法,以近乎实时地创建精确的电离层延迟估计,这是自动执行电离层监测程序的关键。使用2003年11月20日和2004年11月9日电离层风暴的数据检查了该方法的性能。这些结果证明了在长期电离层监测中简化真相处理的有效性。从案例研究中,自动程序成功地识别出极端的电离层异常,包括观察到的两个最严重的电离层梯度,并在之前基于手动分析对其进行了验证。数据处理的自动化使我们能够连续分析电离层数据,并能更准确地对标称和异常条件下的电离层行为进行分类。

著录项

  • 来源
    《Radio Science》 |2012年第4期|p.RS4006.1-RS4006.12|共12页
  • 作者

    Sungwook Jung; Jiyun Lee;

  • 作者单位

    Korea Advanced Institute of Science and Technology, Daejeon, South Korea;

    Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 305-701, South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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