Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

Résumé

To perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient.
Fichier principal
Vignette du fichier
DTIS19060.1580894445.pdf (880.55 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02472384 , version 1 (10-02-2020)

Identifiants

  • HAL Id : hal-02472384 , version 1

Citer

Camille Palmier, Karim Dahia, Nicolas Merlinge, Pierre del Moral, Dann Laneuville, et al.. Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation. FUSION 2019 - International Conference on Information Fusion, Jul 2019, Ottawa, Canada. ⟨hal-02472384⟩
79 Consultations
187 Téléchargements

Partager

Gmail Facebook X LinkedIn More