Titre :

Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons

Auteur(s) :

Rabaoui, Asma [Auteur]
Laboratoire de l'intégration, du matériau au système [IMS]
Viandier, Nicolas [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Marais, Juliette [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Duflos, Emmanuel [Auteur correspondant]
LAGIS-SI
Sequential Learning [SEQUEL]
Vanheeghe, Philippe [Auteur]
LAGIS-SI
Sequential Learning [SEQUEL]

Titre de la revue :

IEEE Transactions on Signal Processing

Pagination :

1638 - 1655

Éditeur :

Institute of Electrical and Electronics Engineers

Date de publication :

2012-04-01

ISSN :

1053-587X

Mot(s)-clé(s) :

Dirichlet Process Mixture
GPS
Particle Filter

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such ...
Lire la suite >In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL