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Correction Method for the Observed Global Navigation Satellite System Ultra-Rapid Orbit Based on Dilution of Precision Values

机译:基于精度值稀释的校正全球导航卫星系统超快速轨道的校正方法

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摘要

For ultra-rapid orbits provided by the Global Navigation Satellite System (GNSS), the key parameters, accuracy and timeliness, must be taken into consideration in real-time and near real-time applications. However, insufficient observations in later epochs appear to generate low accuracy in observed orbits, for which a correlation between the Dilution of Precision (DOP) of the orbit parameters and their accuracy is found. To correct the observed GNSS ultra-rapid orbit, a correction method based on the DOP values is proposed by building the function models between DOP values and the orbit accuracy. With 10-day orbit determination experiments, the results show that the observed ultra-rapid-orbit errors, generated by insufficient observations, can be corrected by 12⁻22% for the last three hours of the observed orbits. Moreover, considering the timeliness constraints in ultra-rapid-orbit determination, a DOP amplification factor is defined to weight the contribution of each tracking station and optimize the station distribution in the orbit determination procedure. Finally, six schemes are designed to verify the method and strategy in determining the ultra-rapid orbit based on one-month observations. The orbit accuracy is found to decrease by 1.27⁻6.34 cm with increasing amplification factor from 5⁻20%. Thus, the observed ultra-orbit correction method proposed is ideal when considering accuracy and timeliness in ultra-rapid orbit determination.
机译:对于全球导航卫星系统(GNSS)提供的超快速轨道,必须在实时和近实时应用中考虑关键参数,准确性和及时性。然而,在后面的时期的观察中的观察不足似乎在观察到的轨道中产生低精度,其中发现轨道参数的精度(DOP)的稀释与其精度之间的相关性。为了纠正观察到的GNSS超快速轨道,通过在DOP值和轨道精度之间构建功能模型来提出基于DOP值的校正方法。通过10天的轨道测定实验,结果表明,观察到的观察不足产生的观察到的超快速轨道误差可以通过观察到的轨道的最后三个小时校正1222%。此外,考虑到超快速轨道确定的时间性约束,定义了DOP放大因子以重量每个跟踪站的贡献并优化轨道确定过程中的站分布。最后,旨在验证六种方案以验证基于一个月的观察确定超快速轨道的方法和策略。发现轨道准确度降低1.27±6.34厘米,增大因子增加5-20%。因此,所观察到的超轨校正方法提出了在考虑超快速轨道确定中的准确性和及时性时的理想选择。

著录项

  • 作者

    Qianxin Wang; Chao Hu; Ya Mao;

  • 作者单位
  • 年度 2018
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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