A Periodically Updated Adaptive Sampling Framework for Marine Mobile Observation Platforms

Abstract

While numerical models have been developed for several years, some of these have been applied to ocean state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms.