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

Juliette Marais 1 Asma Rabaoui 2 Emmanuel Duflos 3, 4, *
* Corresponding author
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
4 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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.
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https://hal.archives-ouvertes.fr/hal-00713044
Contributor : Emmanuel Duflos <>
Submitted on : Thursday, June 28, 2012 - 10:03:39 PM
Last modification on : Thursday, February 21, 2019 - 11:02:54 AM

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Juliette Marais, Asma Rabaoui, Emmanuel Duflos. Studies on DPM for the density estimation of pseudorange noises and evaluations on real data. Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, May 2010, Indian Wells, CA, USA, United States. pp.1154-1161, ⟨10.1109/PLANS.2010.5507234⟩. ⟨hal-00713044⟩

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