Lyu Zhitao, Gao Yang
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2020 Dec 18;20(24):7265. doi: 10.3390/s20247265.
High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.
由于存在严重的多径效应、非视距（NLOS）误差以及较差的卫星可见性和几何结构，在城市环境中使用低成本全球导航卫星系统（GNSS）进行高精度定位仍然是一项重大挑战。GNSS系统通常采用最小二乘（LS）或卡尔曼滤波器（KF）估计器来实现，而合适的权重方案对于获得可靠的导航解决方案至关重要。传统的权重方案基于空间信号测距误差（SISRE）、仰角和C/N0值，在城市环境中这些方案的效果较差，因为这些值无法完全体现观测质量。在本文中，我们提出了一种基于新型多特征支持向量机（SVM）信号分类器的GNSS测量权重方案，以提高城市环境中动态GNSS定位的精度。所提出的新权重方案基于对城市环境中GNSS数据重要特征的识别以及对视距（LOS）和NLOS信号的智能分类。为了验证新提出的权重方案的性能，我们将其应用到了实时单频精密单点定位（SFPPP）系统中。使用低成本单频u-blox M8T GNSS接收机进行的基于车辆的动态测试表明，在水平和垂直方向上，使用新权重方案的定位精度比传统的基于C/N0的权重模型分别提高了65.4%和85.0%，与传统方法相比，新权重方案可以消除大多数在交叉路口和短隧道处出现的位置误差尖峰。它还超过了u-blox M8T内置的基于卫星的增强系统（SBAS）解决方案，甚至比u-blox F9P和Trimble BD982等多频接收机的内置实时动态（RTK）解决方案还要好。
