Non-Linear Filtering Approaches for INS/GPS Integration
Résumé
Navigation with an integrated INS/GPS approach requires to solve a set of nonlinear equations. In this case, nonlinear filtering techniques such as Particle Filtering methods are expected to perform better than the classical, but suboptimal, Extended Kalman Filter. Besides, the INS/GPS model has a conditionally linear Gaussian structure. A Rao-Blackwellization procedure can then be applied to reduce the variance of the state estimates. This paper studies different algorithms combining Rao-Blackwellization and particle filtering for a specific INS/GPS scenario. Simulation results illustrate the performance of these algorithms. The variance of the estimates is also compared to the corresponding posterior Cramer-Rao bound.
Mots clés
radiolocalisation
satellite navigation
Cramer Rao inequality
algorithm performance
simulation
algorithm
state estimation
extended Kalman filter
suboptimal filter
particle filter
nonlinear problems
non linear equation
integrated navigation
GPS system
non linear processing
non linear filtering
équation non linéaire
Kalman étendu
performance algorithme
navigation par satellite
inégalité Cramer Rao
algorithme
estimation état
filtre
filtre sous optimal
filtre particule
problème non linéaire
navigation intégrée
système GPS
traitement non linéaire
filtrage non linéaire
| Origine | Fichiers produits par l'(les) auteur(s) |
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