Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation
| hal.structure.identifier | DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau] | |
| dc.contributor.author | PALMIER, Camille | |
| hal.structure.identifier | DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau] | |
| dc.contributor.author | DAHIA, Karim | |
| hal.structure.identifier | DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau] | |
| dc.contributor.author | MERLINGE, Nicolas | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | DEL MORAL, Pierre | |
| hal.structure.identifier | Naval Group | |
| dc.contributor.author | LANEUVILLE, Dann | |
| hal.structure.identifier | DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau] | |
| dc.contributor.author | MUSSO, Christian | |
| dc.date.accessioned | 2024-04-04T02:57:23Z | |
| dc.date.available | 2024-04-04T02:57:23Z | |
| dc.date.conference | 2019-07-02 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192562 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | en | |
| dc.subject | Bathymétrie | |
| dc.subject | Navigation par correlation de terrain | |
| dc.subject | Filtrage particulaire | |
| dc.subject | ABC | |
| dc.subject.en | Particle filter | |
| dc.subject.en | Navigation | |
| dc.subject.en | A pproximate Bayesian computational | |
| dc.title.en | Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Sciences de l'ingénieur [physics] | |
| dc.subject.hal | Physique [physics] | |
| dc.subject.hal | Mathématiques [math] | |
| dc.subject.hal | Informatique [cs] | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | FUSION 2019 - International Conference on Information Fusion | |
| bordeaux.country | CA | |
| bordeaux.conference.city | Ottawa | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02472384 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | non | |
| hal.conference.end | 2019-07-05 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02472384v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=PALMIER,%20Camille&DAHIA,%20Karim&MERLINGE,%20Nicolas&DEL%20MORAL,%20Pierre&LANEUVILLE,%20Dann&rft.genre=unknown |
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