Due to the limitations of electromagnetic signals, underwater scenarios increase the complexity of developing accurate navigation systems. In the last decades, Ultra-Short BaseLine (USBL) positioning systems have been widely and efficiently used for Autonomous Underwater Vehicles (AUVs) localization, endorsing to be a suitable solution to limit the navigation drift without requiring periodic surfacing for Global Positioning System (GPS) resets. Typically, in the localization context, USBL measurements are exploited as observations within the on-board navigation filter where, most of the time, Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) solutions are employed. In a break-away from the above-mentioned approaches, in this study, the localization task is solved as a Maximum A Posteriori (MAP) estimation problem. The presented solution is validated through the use of data gathered in October 2020 during EUMarineRobots (EUMR) tests in La Spezia (Italy) within the activities of the SEALab, the joint research laboratory between the Naval Support and Experimentation Center (Centro di Supporto e Sperimentazione Navale, CSSN) of the Italian Navy and the Interuniversity Center of Integrated Systems for the Marine Environment (ISME).

Maximum a posteriori estimation for AUV localization with USBL measurements

Bresciani M.;Peralta G.;Costanzi R.
2021-01-01

Abstract

Due to the limitations of electromagnetic signals, underwater scenarios increase the complexity of developing accurate navigation systems. In the last decades, Ultra-Short BaseLine (USBL) positioning systems have been widely and efficiently used for Autonomous Underwater Vehicles (AUVs) localization, endorsing to be a suitable solution to limit the navigation drift without requiring periodic surfacing for Global Positioning System (GPS) resets. Typically, in the localization context, USBL measurements are exploited as observations within the on-board navigation filter where, most of the time, Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) solutions are employed. In a break-away from the above-mentioned approaches, in this study, the localization task is solved as a Maximum A Posteriori (MAP) estimation problem. The presented solution is validated through the use of data gathered in October 2020 during EUMarineRobots (EUMR) tests in La Spezia (Italy) within the activities of the SEALab, the joint research laboratory between the Naval Support and Experimentation Center (Centro di Supporto e Sperimentazione Navale, CSSN) of the Italian Navy and the Interuniversity Center of Integrated Systems for the Marine Environment (ISME).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1114397
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