Kalman filters comparison for vehicle localization data alignment

The aim of this paper is to carry out a comparison between several algorithms of the Kalman filters family for nonlinear systems. Alter having presented the most popular of them and showed its limitations, we introduce some new Kalman filters and compare them for the vehicle localization problem. This comparison is based on the predictive step what corresponds to the worst case that it can occur in vehicle localization. Typically, when we achieve a vehicle tracking, if the tracked vehicle is hidden, corrective data are unavailable and therefore the corrective step is disable (time data alignment)