This paper proposes a novel positioning technique suitable for use in mobile robots in urban environments in which large global navigation satellite system (GNSS) positioning errors occur because of multipath signals. During GNSS positioning, the GNSS satellites that are obstructed by buildings emit reflection and diffraction signals, which are called non-line-of-sight (NLOS) multipath signals. These multipath signals cause major positioning errors. The key concept considered in this paper is the estimation of a user's position using the likelihood of the position hypotheses computed from the GNSS pseudoranges, consisting only of LOS signals based on the analysis of the pseudorange residuals. To determine the NLOS GNSS signals from the pseudorange residuals at the user's position, it is necessary to accurately determine the position before the computation of the pseudorange residuals. This problem is solved using a particle filter. We propose a likelihood estimation method using the Mahalanobis distance between the hypotheses of the user's position computed from only the LOS pseudoranges and the particles. To confirm the effectiveness of the proposed technique, a positioning test was performed in a real-world urban environment. The results demonstrated that the proposed method is effective for accurately estimating the user's position in urban canyons.

翻译：本文提出一种适用于城市环境移动机器人的新型定位技术，该环境中因多径信号导致全球导航卫星系统（GNSS）定位存在较大误差。在GNSS定位过程中，被建筑物遮挡的卫星会发射反射和衍射信号，称为非视距（NLOS）多径信号。这些多径信号会造成重大定位误差。本文的核心思想是通过分析伪距残差，仅利用基于LOS信号计算的GNSS伪距似然值来估计用户位置。要从用户位置的伪距残差中识别NLOS GNSS信号，需在计算伪距残差前准确确定位置。该问题通过粒子滤波器解决。我们提出一种似然估计方法，利用仅由LOS伪距计算的用户位置假设与粒子之间的马氏距离。为验证该技术的有效性，在真实城市环境中进行了定位测试。结果表明，所提方法能在城市峡谷中精确估计用户位置。