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A Simulation System For Gnss Multipath Mitigation Using Spatial Statistical Methods

机译:基于空间统计方法的Gnss多径缓解仿真系统

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Multipath is one of the main causes of degraded position accuracy in the Global Navigation Satellite System (GNSS) because portions of the signals can be reflected by high buildings in dense urban areas. Multipath mitigation techniques based on hardware enhancement or signal processing help to improve GNSS accuracy for high-precision surveying. A Geographic Information System (GIS) is also used in the signal propagation model to predict multipath effects. In addition to these existing approaches, we found that spatial statistical methods are useful in multipath mitigation because the multipath produces a unique spatial distribution of user positions. In this paper, we present a spatial statistics-based simulation system for mitigating multipath and improving the accuracy in GNSS positioning. Multipath tends to be associated with spatial outliers in simulated user positions (SUPs) and contributes little to the spatial clustering of SUPs. Using these spatial characteristics, we developed a method called the "satellite participation ratio in outliers versus cluster" (SPROC) to identify multipath satellites. Once the identified multipath satellites are excluded, a user position is determined using a mean spatial center of the SUPs from the remaining satellites. The effects of such multipath mitigation were validated by examining whether the SPROC method correctly identified multipath satellites and by comparing the position errors with and without SPROC. We demonstrated the applicability of our system with a simulation experiment using a precise ephemeris for the Global Positioning System (GPS) and the orbital parameters for the proposed constellations of the GALILEO and the Quasi-Zenith Satellite System (QZSS). In our simulation of Shinjuku ward in Tokyo, the user-equivalent range error (UERE) other than multipath was virtually generated and added to the multipath delay. The pattern of position errors with and without SPROC showed that the improvements in accuracy were considerable at locations close to buildings.
机译:多径是导致全球导航卫星系统(GNSS)位置精度下降的主要原因之一,因为信号的一部分会被密集城市地区的高层建筑反射。基于硬件增强或信号处理的多径缓解技术有助于提高GNSS的精度,以进行高精度测量。信号传播模型中还使用了地理信息系统(GIS)来预测多径效应。除了这些现有方法外,我们发现空间统计方法在多径缓解中很有用,因为多径会产生用户位置的唯一空间分布。在本文中,我们提出了一种基于空间统计的仿真系统,用于减轻多径并提高GNSS定位的准确性。多径往往与模拟用户位置(SUP)中的空间离群值相关,而对SUP的空间聚类几乎没有贡献。利用这些空间特征,我们开发了一种称为“异常值与群集中的卫星参与率”(SPROC)的方法来识别多径卫星。一旦排除了所标识的多径卫星,就使用来自其余卫星的SUP的平均空间中心来确定用户位置。通过检查SPROC方法是否正确识别了多径卫星并通过比较有无SPROC的位置误差,验证了这种多径缓解的效果。我们通过对全球定位系统(GPS)使用精确星历和GALILEO和拟天顶卫星系统(QZSS)拟议星座的轨道参数进行仿真实验,证明了该系统的适用性。在我们对东京新宿区的模拟中,虚拟生成了多径以外的用户等效范围误差(UERE),并将其添加到多径延迟中。使用和不使用SPROC时的位置误差模式表明,在靠近建筑物的位置,精度的提高是可观的。

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