A data assimilation framework for data-driven flow models enabled by motion tomography

International Journal of Intelligent Robotics and Applications - Tập 3 - Trang 158-177 - 2019
Dongsik Chang1, Catherine R. Edwards2, Fumin Zhang3, Jing Sun1
1Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, USA
2Skidaway Institute of Oceanography, University of Georgia, Savannah, USA
3[school of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA]

Tóm tắt

Autonomous underwater vehicles (AUVs) have become central to data collection for scientific and monitoring missions in the coastal and global oceans. To provide immediate navigational support for AUVs, computational data-driven flow models described as generic environmental models (GEMs) construct a map of the environment around AUVs. This paper proposes a data assimilation framework for the GEM to update the map using data collected by the AUVs. Unlike Eulerian data, Lagrangian data along the AUV trajectory carry time-integrated flow information. To facilitate assimilation of Lagrangian data into the GEM, the motion tomography method is employed to convert Lagrangian data of AUVs into an Eulerian spatial map of a flow field. This process allows assimilation of both Eulerian and Lagrangian data into the GEM to be incorporated in a unified framework, which introduces a nonlinear filtering problem. Considering potential complementarity of Eulerian and Lagrangian data in estimating spatial and temporal characteristics of flow, we develop a filtering method for estimation of the spatial and temporal parameters in the GEM. The observability is analyzed to verify the convergence of our filtering method. The proposed data assimilation framework for the GEM is demonstrated through simulations using two flow fields with different characteristics: (i) a double-gyre flow field and (ii) a flow field constructed by using real ocean surface flow observations from high-frequency radar.

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International Journal of Intelligent Robotics and Applications

Tập 3

158-177

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