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首页> 外文期刊>Journal of Aeronautics Astronautics and Aviation >Mitigation of Ionospheric Effect on Multi-GNSS Positioning with Ionosphere Delay Estimation Using Single-Frequency Measurements of Selected Satellites
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Mitigation of Ionospheric Effect on Multi-GNSS Positioning with Ionosphere Delay Estimation Using Single-Frequency Measurements of Selected Satellites

机译：使用选定卫星的单频测量通过电离层延迟估计来缓解电离层对多GNSS定位的影响

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摘要

Ionosphere delay is the largest source of positioning error for single point positioning using single-frequency receiver of global navigation satellite system (GNSS). To mitigate the effect of ionosphere which varies sometimes rapidly and locally due to the solar activity, we propose estimating the ionosphere delay in the process of positioning calculation by using the ionospheric thin shell model with single-frequency pseudorange measurement of single epoch. Since sufficient number of visible satellites is observable for multi-GNSS positioning, all those measurements are not necessarily used. We propose algorithm of selecting useful satellites by the residual ranging error of pseudorange measurement to mitigate the positioning error. The performance evaluation for proposed selection algorithm and ionosphere delay estimation was conducted by using L1 pseudorange measurements of global positioning system (GPS) and GLONASS on both quiet and disturbed ionospheric conditions. Proposed selection algorithm reduced the rms of horizontal positioning error by about 10% on disturbed condition. Proposed ionosphere delay estimation increased the average of horizontal error reduction by 30% in daytime on disturbed condition, compared with the conventional correction method using Klobuchar model.
机译：电离层延迟是使用全球导航卫星系统（GNSS）的单频接收机进行单点定位的最大定位误差源。为了减轻电离层有时因太阳活动而迅速而局部变化的影响，我们建议通过使用电离层薄壳模型和单历时单频伪距测量来估算定位计算过程中的电离层延迟。由于可以观测到足够数量的可见卫星用于多重GNSS定位，因此不必使用所有这些测量。我们提出了通过伪距测量的剩余测距误差来选择有用卫星的算法，以减轻定位误差。通过在安静和受干扰的电离层条件下使用全球定位系统（GPS）和GLONASS的L1伪距测量，对建议的选择算法和电离层延迟估计进行了性能评估。提出的选择算法在干扰条件下将水平定位误差的均方根值降低了约10％。与传统的使用Klobuchar模型的校正方法相比，拟议的电离层延迟估计将白天在受干扰条件下的水平误差平均降低了30％。

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