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Commonplace in oceanography is the collection of ocean drifter positions.Ocean drifters are devices that sit on the surface of the ocean and move withthe flow, transmitting their position via GPS to stations on land. Usingdrifter data, it is possible to obtain a posterior on the underlying flow. Thisproblem, however, is highly underdetermined. Through controlling an oceandrifter, we attempt to improve our knowledge of the underlying flow. We do thisby instructing the drifter to explore parts of the flow currently uncharted,thereby obtaining fresh observations. The efficacy of a control is determinedby its effect on the variance of the posterior distribution. A smaller varianceis interpreted as a better understanding of the flow. We show a systematicreduction in variance can be achieved by utilising controls that allow thedrifter to navigate new or interesting flow structures, a good example of whichare eddies.

Decreasing flow uncertainty in Bayesian inverse problems through Lagrangian drifter control