In the aftermath of radiation or chemical accidents, responders must rapidly map out regions of contamination as quickly and accurately as possible. One important and relevant statistical method for this kind of disaster response is spatial kriging, which makes predictions based on incomplete knowledge of spatially referenced observations. Given an Unmanned Aerial System (UAS) equipped with radiation sensors, we develop a spatial statistics-based approach to optimally map out a contamination field over a geographic region. In this article, we evaluate three approaches to UAS mapping: a Variance Driven Sampling (VDS) approach that minimizes kriging variance, a more computationally intensive Hybrid Entropy Search (HES), and a baseline Levy Flight search. Considering limited UAS range, we also implement a restricted version of these approaches that only considers nearby points. We find that HES is optimal for small numbers of sampled points with the restricted versions of HES and VDS becoming optimal for larger samples. Ultimately, the best method is dependent on the number of samples to be taken, with each method providing clear benefits over a random search in terms of both mean squared error and path length. We demonstrate the advantages of our methodology using actual radiation field test data from the Idaho National Lab.

在发生辐射或化学事故后，响应者必须迅速，准确地绘制出污染区域。对于这种灾难响应，一种重要且相关的统计方法是空间克里金法，它基于对空间参考观测值的不完全了解来进行预测。给定配备了辐射传感器的无人机系统（UAS），我们将开发一种基于空间统计的方法，以最佳地绘制出地理区域内的污染场。在本文中，我们评估了UAS映射的三种方法：方差驱动采样（VDS）方法，该方法最大程度地减小了克里金法的方差，计算量更大的混合熵搜索（HES）和基准征税飞行搜索。考虑到有限的UAS范围，我们还实施了这些方法的受限版本，仅考虑附近的点。我们发现，HES对于少量采样点是最佳的，而HES和VDS的受限版本对于较大样本而言是最佳的。最终，最好的方法取决于要采样的数量，每种方法在均方误差和路径长度方面都比随机搜索有明显的好处。我们使用爱达荷州国家实验室的实际辐射场测试数据证明了我们方法的优势。与每种方法相比，每种方法在均方误差和路径长度方面均具有明显的优势。我们使用爱达荷州国家实验室的实际辐射场测试数据证明了我们方法的优势。与每种方法相比，每种方法在均方误差和路径长度方面均具有明显的优势。我们使用爱达荷州国家实验室的实际辐射场测试数据证明了我们方法的优势。