Determination of Pseudorange Error Models and Multipath Characterization under Signal-Degraded Scenarios
Determination of Pseudorange Error Models and Multipath Characterization under Signal-Degraded Scenarios
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DOI：
10.33012/2018.16094
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发表时间：
2018-09
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影响因子：
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通讯作者：
D. Medina;Kasia Gibson;R. Ziebold;P. Closas
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作者：
D. Medina;Kasia Gibson;R. Ziebold;P. Closas
随着全球导航卫星系统（GNSS）在自主交通中的应用变得越来越重要，GNSS信号中不确定性的表征一直是GNSS社区反复讨论的话题。在具有挑战性的场景中，多路径是导航时最明显的错误来源，因此对这一过程进行特征描述的需求越来越大。不适当的测量方差建模可以降低估计的准确性，就像这些测量的总偏差一样。因此，需要有可靠的和统计上有意义的误差建模。为了将多径与其他误差源隔离，采用了双频离子校正码-载波组合（CMC）。采用共轭先验贝叶斯分析对多径效应的方差进行表征，进而对误差模型的未知参数进行回归。为了推导这些模型，收集了大量的多频GPS和伽利略数据。在定位和完整性监测性能方面，将所提出的模型与来自具有挑战性的信号退化场景的实际数据进行了比较。
As Global Navigation Satellite Systems (GNSS) become increasingly important for the application of autonomous transportation, the characterization of the uncertainty present in GNSS signals has been a recurrent topic in the GNSS community. With multipath being the most pronounced source of error while navigating during challenging scenarios, characterizing this process is a growing requirement. Improper modeling of the measurements variances can reduce the accuracy of the estimation as much as a gross bias on these measurements. It is therefore required to have reliable and statistically meaningful modeling of errors. To isolate the multipath from the other error sources, the dual-frequency iono-corrected code-minus-carrier combination (CMC) is employed. Conjugate prior Bayesian analysis is used to characterize the variance of the multipath effect to later regress the unknown parameter of the proposed error models. For the derivation of these models, massive amounts of multi-frequency GPS and Galileo data was collected. The comparison between the proposed models was carried out with actual data from a challenging signal-degraded scenario, in terms of positioning and integrity monitoring performance.