  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.
