Robust GNSS Navigation in Urban Environments by Bounding NLOS Bias of GNSS Pseudoranges Using a 3D City Model

The well-known conventional Weighted Least Squares (WLS) and extended Kalman filter (EKF) are the standard estimation methods for positioning with GNSS measurements. However, this estimators are not optimal when the GNSS measurements become contaminated by non-Gaussian errors including multipath (MP) and nonline-of-sight (NLOS) biases. In this paper, we use an additional information of the geometric environment provided by a 3D model to build-up a robust solution against biases which may be summed up from MP and NLOS signals in urban environments. We first use a 3D city model to predict lower and upper bounds of these biases. Then, we integrate this information in the position estimation problem. We investigated in two ways of making use of this additional information: the first one is to consider these biases as additive noise and exploiting the bounds to end up with a constrained state estimation by WLS or Kalman filter. The second way is to investigate in the maximum likelihood estimation of both the MP-NLOS bias and the state ending up with a less accurate but acceptable solution. Test results using real GPS signal in Toulouse show that these estimators capable of improving the positioning accuracy compared to the conventional WLS if the NLOS bounds are well-chosen.