Non-Linear Filtering Approaches for INS/GPS Integration

Audrey Giremus (1) , Arnaud Doucet (2) , Anne-Christine Escher (3) , Jean-Yves Tourneret (1)

1 ENSEEIHT - Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications
2 Signal Processing Laboratory
3 ENAC - Ecole Nationale de l'Aviation Civile

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

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

Audrey Giremus, Arnaud Doucet, Anne-Christine Escher, Jean-Yves Tourneret. Non-Linear Filtering Approaches for INS/GPS Integration. EUSIPCO 2004, 12th European Signal Processing Conference, Sep 2004, Vienna, Austria. pp 873-876. ⟨hal-01021735⟩