Studies on DPM for the density estimation of pseudorange noises and evaluations on real data

Abstract

GNSS localization is accurate in clear environment where the pseudorange noise distributions are assumed white- Gaussian. But in constricted environment, e.g. dense urban environment, because of the signal reflections on the surrounding obstacles, this assumption cannot be used and accuracy and continuity of service of GNSS receivers are strongly degraded. To enhance the localization performances, we propose to use Dirichlet Process Mixtures to model the pseudorange error density at each acquisition step. Next, this estimation will be used in Rao-Blackwellized Particle Filter to compute the position. This sequential estimation is adapted when the noise is non-stationary. This approach will be tested on real data acquired by a single frequency receiver.