A moving fixed-interval filter/smoother for estimation of vehicle position using odometer and map-matched GPS

Cindie Andrieu (1) , Guillaume Saint Pierre (2) , Xavier Bressaud (3)

1 IFSTTAR/COSYS/LIVIC - Laboratoire sur les Interactions Véhicules-Infrastructure-Conducteurs
2 IFSTTAR/LIVIC - Laboratoire sur les Interactions Véhicules-Infrastructure-Conducteurs
3 IMT - Institut de Mathématiques de Toulouse UMR5219

This paper presents some optimal real-time and post-processing estimators of vehicle position using odometer and map-matched GPS measurements. These estimators were based on a simple statistical error model of the odometer and the GPS which makes the model generalizable to other applications. Firstly, an asymptotically minimum variance unbiased estimator and two optimal moving fixed interval filters which are more flexibles are exposed. Then, the post-processing case leads to the construction of two moving fixed interval smoothers. These estimators are tested and compared with the classical Kalman filter with simulated and real data, and the results show a good accuracy of each of them.

Mots clés: Vehicle position; Navigation; Moving-fixed interval filter; Kalman filter

Domaines: Applications [stat.AP]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Cindie Andrieu, Guillaume Saint Pierre, Xavier Bressaud. A moving fixed-interval filter/smoother for estimation of vehicle position using odometer and map-matched GPS. 2013. ⟨hal-00915402⟩