Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

International audienceTo 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

Navigation à l'aide d'un gravimètre atomique

International audienceCold atom interferometer is a promising technology to obtain a highly sensitive and accurate absolute gravimeter. With the help of an anomalies gravity map, local measurements of gravity allow a terrain-based navigation. This paper follows the one we published in Fusion 2017. Based on an atomic gravimeter we present a method to map the gravity anomaly. We propose a modification of the Laplace-based particle filter so as to make it more robust. Some simulation results demonstrate a better robustness of the proposed filter.L'interférométrie à atomes froids est une technologie prometteuse pour obtenir un gravimètre absolu de grande sensibilité et précision. A partir d'une carte d'anomalies gravimétriques, la mesure locale de la gravité permet une navigation par corrélation de terrain. Ce papier fait suite à celui publié au congrès Fusion 2017. Nous présentons une méthode d'élaboration de cartes d’anomalies gravimétriques à partir du gravimètre atomique. Nous proposons une modification du filtre Particulaire de Laplace qui offre une meilleure robustesse. Des résultats de simulation montrent une meilleure robustesse de ce filtre

Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

International audienceTo 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

Comparaison de deux techniques de trachéotomie percutanée dans un service de réanimation polyvalente (trachéotomie par dilatation forceps versus trachéotomie par dispositif rotatoire PercuTwist R à partir de 61 patients)

BORDEAUX2-BU Santé (330632101) / SudocPARIS-BIUM (751062103) / SudocPARIS-Bib. Serv.Santé Armées (751055204) / SudocSudocFranceF

Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

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