Graduate School and Research Center in Digital Sciences

Bayesian fusion of GNSS, ITS-G5 and IR-UWB data for robust cooperative vehicular localization

Hoang, Gia Minh; Denis, Benoît; Härri, Jérôme; Slock, Dirk

URSI-France 2018 Workshop: Geolocation and navigation, Journées URSI-France 2018 / Also Published in "Comptes Rendus Physique", 8 April 2019

In the automotive domain, Cooperative Localization (CLoc) is a new promising paradigm that aims at outperforming conventional Global Navigation Satellite Systems (GNSS) in terms of positioning accuracy, robustness, and service continuity, by relying on Vehicle-to-Vehicle (V2V) communications and hybrid data fusion. However, the growing number and the variety of the sensors aboard vehicles raise unprecedented challenges, especially in the context of distributed fusion approaches. This paper thus compares parametric and nonparametric Bayesian data fusion engines (e.g., based on cooperative variants of the Extended Kalman Filter (EKF) and Particle Filter (PF), respectively), while validating a CLoc scheme suitable to Vehicular Ad Hoc Networks (VANETs). More particularly, absolute position information from both onboard GNSS receiver and ITS-G5 V2V messages, as well as relative distance measurements based on the Impulse Radio-Ultra-Wideband (IR-UWB) technology, are combined into a single location solution that is hopefully more robust and more accurate than that of standalone GNSS. First, we investigate V2V ranging accuracy on a highway under real mobility conditions. In the same environment, we then provide offline validations of CLoc positioning, confirming significant performance gains through cooperation over conventional GNSS, even in case of poor initialization. In this specific context, the PF solution is thus shown to yield even better accuracy in comparison with EKF, thanks to its fine robustness against faced non-linear dynamics and non-Gaussian noise processes. Finally, we illustrate the resilience of the proposed solution under temporary GNSS denial.

Document Doi Bibtex

Title:Bayesian fusion of GNSS, ITS-G5 and IR-UWB data for robust cooperative vehicular localization
Keywords:Bayesian data fusion, Cooperative localization, Impulse radio-ultra wideband, ITS-G5, Vehicle-to-vehicle data communications, Vehicular ad hoc NETwork (VANET)
Type:Journal
Language:English
City:
Date:
Department:Communication systems
Eurecom ref:5850
Copyright: © Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in URSI-France 2018 Workshop: Geolocation and navigation, Journées URSI-France 2018 / Also Published in "Comptes Rendus Physique", 8 April 2019 and is available at : http://doi.org/10.1016/j.crhy.2019.03.004
Bibtex: @article{EURECOM+5850, doi = {http://doi.org/10.1016/j.crhy.2019.03.004}, year = {2019}, month = {04}, title = {{B}ayesian fusion of {GNSS}, {ITS}-{G}5 and {IR}-{UWB} data for robust cooperative vehicular localization}, author = {{H}oang, {G}ia {M}inh and {D}enis, {B}eno{\^i}t and {H}{\"a}rri, {J}{\'e}r{\^o}me and {S}lock, {D}irk}, journal = {{URSI}-{F}rance 2018 {W}orkshop: {G}eolocation and navigation, {J}ourn{\'e}es {URSI}-{F}rance 2018 / {A}lso {P}ublished in "{C}omptes {R}endus {P}hysique", 8 {A}pril 2019}, url = {http://www.eurecom.fr/publication/5850} }
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