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Learning a Detection Map for a Network of Unattended Ground Sensors

机译：学习无人值守地面传感器网络的检测图

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We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.
机译：我们已经开发了算法，可以自动了解虚拟存在和扩展防御（VPED）系统的已部署传感器场的检测图，而无需事先了解本地地形。 VPED系统是一个无人值守的传感器吊舱网络，每个吊舱都包含声学和地震传感器。每个吊舱都有能力在有限范围内检测和分类移动目标。通过使用豆荚网络，我们可以形成一个虚拟边界，每个豆荚负责该边界的特定部分。该地点的地理和土壤条件可能会影响豆荚的检测性能。因此，现场网络可能不具有与实验室设计的网络相同的性能。为了解决此问题，当移动部署单元（MDU）将其安装在站点上时，我们会自动估计网络的检测性能。 MDU将配备GPS装置，因此系统不仅知道何时可以检测到MDU，而且还知道MDU的位置。在本文中，我们演示了如何处理各向异性的传感器配置，地理和土壤条件。

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