Performances Analysis of GNSS NLOS Bias Correction in Urban Environment Using a Three-Dimensional City Model and GNSS Simulator

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The well-known conventional least squares (LS) and extended Kalman filter (EKF) are ones of the most widely used algorithms in science and particularly in localization with global navigation satellites systems (GNSS) measurements. However, these estimators are not optimal when the GNSS measurements become contaminated by nonGaussian errors including multipath (MP) and nonline-of-sight (NLOS) biases. On the other hand, this kind of ranging measurements errors occurs generally in urban areas where GNSS-based positioning applications require more accuracy and reliability. In this paper, we use additional information of the environment consisting of bias prediction from a three-dimensional (3-D) model and a GNSS simulator to exploit constructively NLOS measurements. We use this 3-D GNSS simulator to predict lower and upper bounds of these biases. Then, we integrate this information in the position estimation problem by considering these biases as additive error and exploiting the bounds to end-up with a constrained state estimation problem that we resolve with existing constrained least squares (CLS) and constrained EKF (CEFK) algorithms. Experimental results using real GPS signals in down-town Toulouse show that the proposed estimator is capable of improving the positioning accuracy compared with conventional algorithms. Theoretical conditions have been established to determine the acceptable bias prediction error allowing better positioning performance than conventional estimators. Tests are conducted then to validate these conditions and investigate the influence of the bias prediction error on the localization performance by proposing new accuracy metrics.