非视线传播
全球导航卫星系统应用
计算机科学
多径传播
水准点(测量)
人工智能
机器学习
多路径缓解
卫星系统
实时计算
全球定位系统
电信
频道(广播)
无线
大地测量学
地理
作者
Penghui Xu,Guohao Zhang,Bo Yang,Li–Ta Hsu
出处
期刊:IEEE Aerospace and Electronic Systems Magazine
[Institute of Electrical and Electronics Engineers]
日期:2024-04-30
卷期号:39 (9): 26-44
被引量:2
标识
DOI:10.1109/maes.2024.3395182
摘要
Global navigation satellite system (GNSS) positioning accuracy is degraded in urban canyons due to signal blockages and reflections, which is still a major challenge. Recently, using machine learning to improve the accuracy of GNSS positioning in urban areas has become a new trend. This paper summarizes the works focused on GNSS multipath/non-light-of-sight (NLOS) mitigation using machine learning. The review of the studies is categorized based on the input features, algorithms, and outputs. The categorization shows that the received signal strength, elevation angle, and receiver correlator outputs from a single channel of satellite signal are the most popular input features. For the algorithm selection, the support vector machine and fully connected neural network (FCNN) are the algorithms most widely used. In terms of the outputs, most of the works made improvements in measurement status prediction, namely, LOS, multipath, and NLOS. Besides, this paper also provides an open-source dataset with four scenarios for machine learning algorithms for the GNSS multipath/NLOS mitigation. Finally, the benchmarks are established based on the proposed dataset and the FCNN and least-squares estimation to enable performance evaluation in Kaggle.
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