论文标题
学习矢量值高斯随机场的学习游览集用于自动海洋采样
Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling
论文作者
论文摘要
对于海洋科学和海洋资源管理，改善和优化海洋学抽样是至关重要的任务。面对有限的资源在理解水柱中的过程中，统计和自治系统的结合为实验设计提供了新的机会。在这项工作中，我们开发了有效的空间采样方法，用于表征由几个响应的规定阈值所定义的区域，并应用于根据温度和盐度测量值绘制沿海海洋现象的应用。具体而言，我们根据矢量值高斯随机场的不确定性定义了设计标准，并在此类框架中为预期的综合Bernoulli方差降低而得出可拖动表达式。我们证明了如何使用此标准来优先考虑模棱两可的位置采样工作，从而使探索更有效。我们使用仿真来研究和比较所考虑方法的特性，然后是野外部署和自动水下车辆的结果，作为研究映射河羽界边界的研究的一部分。结果表明，将统计方法和机器人平台相结合以有效地告知和执行数据驱动的环境采样的潜力。
Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.
