Estimation of Spatially-Correlated Ocean Currents from Ensemble Forecasts and Online Measurements
This work addresses ocean current estimation for marine robotics applications like path planning, representing an incremental improvement through hybrid methods.
The authors tackled the problem of estimating two-dimensional, time-invariant ocean currents by combining ensemble forecasts and online measurements, resulting in a computationally efficient method validated with real-world data and performance comparisons.
We present a method to estimate two-dimensional, time-invariant oceanic flow fields based on data from both ensemble forecasts and online measurements. Our method produces a realistic estimate in a computationally efficient manner suitable for use in marine robotics for path planning and related applications. We use kernel methods and singular value decomposition to find a compact model of the ensemble data that is represented as a linear combination of basis flow fields and that preserves the spatial correlations present in the data. Online measurements of ocean current, taken for example by marine robots, can then be incorporated using recursive Bayesian estimation. We provide computational analysis, performance comparisons with related methods, and demonstration with real-world ensemble data to show the computational efficiency and validity of our method. Possible applications in addition to path planning include active perception for model improvement through deliberate choice of measurement locations.