Ecole d'ingénieur et centre de recherche en Sciences du numérique

Robust and low complexity bayesian data fusion for hybrid cooperative vehicular localization

Hoang, Gia-Minh; Denis, Benoit; Härri, Jérôme; Slock, Dirk TM

ICC 2017, IEEE International Conference on Communications, May 21-25, 2017, Paris, France

This paper addresses Particle Filter (PF)-based hybrid Cooperative Localization (CLoc) strategies consisting of fusing absolute position information from embedded Global Navigation Satellite System (GNSS) with relative distance-dependent estimates using Impulse Radio - Ultra WideBand (IR-UWB) technology. Such hybrid GNSS/IR-UWB CLoc yet cannot benefit from the high precision estimates from the IR-UWB due to the disparity between GNSS position and IR-UWB V2V range measurement noises, leading to a divergence in CLoc accuracy. This paper first investigates the source of such counter-intuitive effect, and second proposes a novel adaptive Bayesian dithering technique to improve the efficiency of GNSS/IR-UWB fusionbased CLoc. This strategy increases the probability to reach a 20 cm accuracy from 50% (conventional IR-UWB and WiFi PF) to 95%.

Document Doi Bibtex

Titre:Robust and low complexity bayesian data fusion for hybrid cooperative vehicular localization
Type:Conférence
Langue:English
Ville:Paris
Pays:FRANCE
Date:
Département:Systèmes de Communication
Eurecom ref:5138
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Bibtex: @inproceedings{EURECOM+5138, doi = {http://dx.doi.org/10.1109/ICC.2017.7996985}, year = {2017}, title = {{R}obust and low complexity bayesian data fusion for hybrid cooperative vehicular localization}, author = {{H}oang, {G}ia-{M}inh and {D}enis, {B}enoit and {H}{\"a}rri, {J}{\'e}r{\^o}me and {S}lock, {D}irk {TM}}, booktitle = {{ICC} 2017, {IEEE} {I}nternational {C}onference on {C}ommunications, {M}ay 21-25, 2017, {P}aris, {F}rance}, address = {{P}aris, {FRANCE}}, month = {05}, url = {http://www.eurecom.fr/publication/5138} }
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