Wang Hao, Pan Shuguo, Gao Wang, Xia Yan, Ma Chun
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing 210096, China.
Micromachines (Basel). 2022 Jul 17;13(7):1128. doi: 10.3390/mi13071128.
Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.
由于大量的多径效应和非视距（NLOS）信号接收，在高度城市化地区，全球导航卫星系统（GNSS）定位解算的准确性和可靠性会严重下降，这对GNSS/惯性导航系统（INS）组合导航的性能产生负面影响。因此，本文提出一种基于K均值聚类算法的多径/NLOS检测方法，用于车辆GNSS/INS组合定位。该方法综合考虑了从GNSS原始观测中导出的不同特征参数，如卫星仰角、载波噪声比、伪距残差和伪距率一致性，以有效地对GNSS信号进行分类。鉴于不同GNSS信号对定位结果的影响，利用K均值聚类算法将观测数据分为两大类：直接信号和间接信号（包括多径信号和NLOS信号）。然后，从观测数据中分离出多径/NLOS信号。最后，本文使用实测的车辆GNSS/INS观测数据，包括离线数据集和在线数据集，基于双差伪距定位验证信号分类的准确性。在典型城市场景中进行的一系列实验表明，与传统的GNSS/INS组合导航相比，该方法可显著提高定位精度。在排除GNSS异常值后，离线数据集在水平和垂直方向的定位精度分别提高了16%和85%，在线数据集在这两个方向的定位精度分别提高了21%和41%。该方法不依赖外部地理信息数据和其他传感器，具有更好的实用性和环境适应性。
