Abstract
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.
Highlights
Autonomous underwater vehicles (AUVs) are proven versatile instruments for ocean sampling and monitoring (Curtin et al 1993; Fratantoni and Haddock 2009; Leonard et al 2010)
To facilitate assimilation of Lagrangian data collected by AUVs into the generic environmental models (GEMs), we convert the data into an Eulerian spatial map through motion tomography (MT) so that Lagrangian data assimilation can be achieved together with Eulerian data assimilation in a unified framework
This paper has presented a unified framework for assimilating both Lagrangian and Eulerian data into data-driven computational flow models described as generic environmental models (GEMs)
Summary
Autonomous underwater vehicles (AUVs) are proven versatile instruments for ocean sampling and monitoring (Curtin et al 1993; Fratantoni and Haddock 2009; Leonard et al 2010). By deriving an expression of this deviation using a vehicle motion model, MT constructs a system of equations that describes the influence of flow on the vehicle trajectory Through solving this system of equations, MT converts Lagrangian data into an Eulerian spatial map of a flow field, allowing Lagrangian data to be incorporated together with Eulerian data into a unified data assimilation framework.
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