Ghalia Nassreddine

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Multiple model methods have been generally considered as the mainstream approach for estimating the state of dynamic systems under motion model uncertainty. In this paper, a multiple model method based on belief function theory is proposed. This method handles the case of systems with an unknown and variant motion model. First, a set of candidate models is(More)
The goal of map-matching algorithms is to identify the road taken by a vehicle and to compute an estimate of the vehicle position on that road using a digital map. In this paper, a map-matching algorithm based on interval analysis and the belief function theory is proposed. The method combines the outputs from existing bounded-error estimation techniques(More)
Map matching algorithms are used to integrate an initial estimated position with digital road network data for computing the vehicle position on a road map. In this paper, a map matching algorithm based on belief function theory is proposed. This method provides an accurate estimation of vehicle position relative to a digital road map using belief function(More)
A new approach to nonlinear state estimation based on belief-function theory and interval analysis is presented. This method uses belief structures composed of a finite number of axis-aligned boxes with associated masses. Such belief structures can represent partial information on model and measurement uncertainties more accurately than can the(More)
The goal of state estimation method is to compute an accurate estimation of the state of the system based on the measurement given by different sensors and a mathematical representation of the system. In this paper a new state estimation method based on Dampster-Shafer theory and interval analysis is presented. This method uses belief structures composed of(More)
The goal of map matching methods is to compute an accurate position of a vehicle from an initial estimated position using a digital road network data. In this paper, a new map matching method based on Dempster-Shafer theory and interval analysis is presented. The core idea of this method is the use of Dempster-Shafer theory for modeling partial information(More)
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