%0 Journal Article
%T A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization
%A Adda Redouane Ahmed Bacha
%A Dominique Gruyer
%A Alain Lambert
%J Positioning
%P 271-281
%@ 2150-8526
%D 2013
%I Scientific Research Publishing
%R 10.4236/pos.2013.44027
%X <p>
	<span style=\"font-family:Verdana;\">In this paper</span><span style=\"font-family:Verdana;\">,</span><span style=\"font-family:Verdana;\"> an innovative collaborative data
fusion approach </span><span style=\"font-family:Verdana;\">to</span><span style=\"font-family:Verdana;\"> ego-vehicle localization is presented. This approach called Optimized Kalman
Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost
GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is
developed addressing the challenge of managing reactiv</span><span style=\"font-family:Verdana;\">ity and robustness during a real time
ego-localization process. For ego-vehicle localization, especially for highly dy</span><span style=\"font-family:Verdana;\">namic on-road maneuvers, a filter
needs to be robust and reactive at the same time. In these situations, the
balance be</span><span style=\"font-family:Verdana;\">tween reactivity
and robustness concepts is crucial. The OKS filter represents an intelligent
cooperative-reactive local</span><span style=\"font-family:Verdana;\">ization algorithm inspired by dynamic Particle Swarm Optimization (PSO).
It combines advantages coming from two filters: Particle Filter (PF) and
Extended Kalman filter (EKF). The OKS is tested using real embedded sensors
data collected in the Satory¡¯s test tracks. The OKS is also compared with both
the well</span><span style=\"font-family:Verdana;\">-</span><span style=\"font-family:Verdana;\">known EKF and the
Particle Filters (PF). The results show the efficiency of the OKS for a high
dynamic driving scenario with damaged and low quality GPS data</span><span style=\"font-family:Verdana;\">.</span><span></span>
</p>
%K Localization
%K Mobile Robotic
%K Kalman Filter
%K EKF
%K Particle Swarm Optimization
%K PSO
%K Particle Filter
%K Data Fusion
%K Vehicle Positioning
%K Navigation
%K GPS
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=39553