Shekaramiz Mohammad, Moon Todd K, Gunther Jacob H
ECE Department and Information Dynamics Laboratory, Utah State University.
Conf Rec Asilomar Conf Signals Syst Comput. 2017;51:885-889. doi: 10.1109/ACSSC.2017.8335476. Epub 2017 Oct 29.
We consider configuration of multiple mobile sensors to explore and refine knowledge in an unknown field. After some initial discovery, it is desired to collect data from the regions that are far away from the current sensor trajectories in order to favor the exploration purposes, while simultaneously, exploring the vicinity of known interesting phenomena to refine the measurements. Since the collected data only provide us with local information, there is no optimal solution to be sought for the next trajectory of sensors. Using Gaussian process regression, we provide a simple framework that accounts for both the conflicting data refinement and exploration goals, and to make reasonable decisions for the trajectories of mobile sensors.
我们考虑使用多个移动传感器进行配置，以在未知领域中探索并完善知识。经过一些初步探索后，为了便于开展探索工作，希望从远离当前传感器轨迹的区域收集数据，同时，对已知有趣现象的附近区域进行探索，以完善测量结果。由于收集到的数据仅为我们提供局部信息，因此无法为传感器的下一个轨迹找到最优解。我们使用高斯过程回归提供了一个简单的框架，该框架兼顾了相互冲突的数据完善和探索目标，并为移动传感器的轨迹做出合理决策。
