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Integration of a strapdown gravimeter system in an autonomous underwater vehicle

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  • Additional Information
    • Contributors:
      Laboratoire Géomatique et foncier (GeF); Conservatoire National des Arts et Métiers CNAM (CNAM); HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM); École supérieure des géomètres et topographes (ESGT-CNAM); Laboratoire Géosciences Océan (LGO); Université de Bretagne Sud (UBS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS); Direction Générale de l'Armement (DGA); Région des Pays de le Loire; Conservatoire National des Arts et Métiers (Cnam)
    • Publication Information:
      HAL CCSD
      Copernicus GmbH (Copernicus Publications)
    • Publication Date:
      2015
    • Collection:
      Université de Bretagne Occidentale: HAL
    • Abstract:
      International audience ; We present a new mobile instrument for measuring dynamically the gravity vector and its gradients in underwater environment, named GRAVIMOB. Our instrument is a strapdown sensor, consisted of electrostatic accelerometers installed in a waterproof sphere. It is designed to be embedded in an Autonomous Underwater Vehicle (AUV). Since the positioning of an AUV is approximate in underwater environment, the key issue raised here is to estimate the uncertainty in the gravity field resulting from the use of such position data. This paper focuses on the assessment of the system performances. The latter have been studied by simulation with reference data calculated from actual submarine geological structures, on which different noise models have been added. Results show that spatial evolutions of the gravity field and statistical properties of stochastic processes affecting the measurements have to be considered carefully in order the minimize the error. The Unscented Kalman Filter (UKF) has been favored to the Extended Kalman Filter (EKF) by its ease of implementation and its better robustness to non-linearities.
    • Relation:
      hal-03559412; https://hal.science/hal-03559412; https://hal.science/hal-03559412/document; https://hal.science/hal-03559412/file/isprsarchives-XL-5-W5-199-2015.pdf
    • Accession Number:
      10.5194/isprsarchives-xl-5-w5-199-2015
    • Online Access:
      https://doi.org/10.5194/isprsarchives-xl-5-w5-199-2015
      https://hal.science/hal-03559412
      https://hal.science/hal-03559412/document
      https://hal.science/hal-03559412/file/isprsarchives-XL-5-W5-199-2015.pdf
    • Rights:
      http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.E49BD547