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Machine learning based LOS/NLOS classifier and robust estimator for GNSS shadow matching
Satellite Navigation ( IF 11.2 ) Pub Date : 2020-05-11 , DOI: 10.1186/s43020-020-00016-w
Haosheng Xu , Antonio Angrisano , Salvatore Gaglione , Li-Ta Hsu

Global Navigation Satellites Systems (GNSS) is frequently used for positioning services in various applications, e.g., pedestrian and vehicular navigation. However, it is well-known that GNSS positioning performs unreliably in urban environments. GNSS shadow matching is a method of improving accuracy in the cross-street direction. Initial position and classification of observed satellite visibility between line-of-sight (LOS) and non-line-of-sight (NLOS) are essential for its performance. For the conventional LOS/NLOS classification, the classifiers are based on a single feature, extracted from raw GNSS measurements, such as signal noise ratio, pseudorange, elevation angle, etc. Especially in urban canyons, these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding buildings. Besides, the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban areas. In our study, shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine (SVM). The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5% in urban scenarios. An important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be misclassified. By solving this problem, the SVM classification shows the potential of about 90% classification accuracy for various urban cases. With the help of these approaches, the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction; these performances are suitable for urban positioning.

中文翻译:

基于机器学习的LOS / NLOS分类器和鲁棒估计器,用于GNSS阴影匹配

全球导航卫星系统(GNSS)通常用于各种应用中的定位服务,例如行人和车辆导航。但是,众所周知,GNSS定位在城市环境中表现不可靠。GNSS阴影匹配是一种提高跨街方向准确性的方法。视线(LOS)和非视线(NLOS)之间的卫星可见度的初始位置和分类对于其性能至关重要。对于常规的LOS / NLOS分类,分类器基于单个特征,并从原始GNSS测量中提取,例如信号噪声比,伪距,仰角等。特别是在城市峡谷中,这些测量不稳定且不可靠。周围建筑物的信号反射和折射。除了,传统的最小二乘定位方法不足以为城市中的阴影匹配提供准确的初始化。在我们的研究中,使用鲁棒估计器的初始位置和由支持向量机(SVM)确定的卫星可见度,可以改善阴影匹配。鲁棒估计器具有更高的定位精度,在城市场景中,SVM分类的分类率可以达到91.5%。一个重要的问题与具有超高或低仰角的卫星以及建筑物边界附近的卫星很可能会被错误分类有关。通过解决此问题,SVM分类显示出各种城市案例的分类精度约为90%的潜力。在这些方法的帮助下,阴影匹配的平均误差为10.27 m,值为1。横街方向44 m;这些表演适合城市定位。
更新日期:2020-05-11
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