GNSS pseudorange error density tracking using Dirichlet Process Mixture

Nicolas Viandier 1 Asma Rabaoui 2 Juliette Marais 1 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 : In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.
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https://hal.archives-ouvertes.fr/hal-00713046
Contributor : Emmanuel Duflos <>
Submitted on : Thursday, June 28, 2012 - 10:23:06 PM
Last modification on : Thursday, February 21, 2019 - 11:02:54 AM

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  • HAL Id : hal-00713046, version 1

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Nicolas Viandier, Asma Rabaoui, Juliette Marais, Emmanuel Duflos. GNSS pseudorange error density tracking using Dirichlet Process Mixture. FUSION 2010, Jul 2010, Edinburgh, United Kingdom. pp.1-7. ⟨hal-00713046⟩

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