Screening maritime shipping containers for weapons of mass destruction

Christina M. Rudzinski, D. Masters, A. Buck, Martha Wall, David Tremblay, E. Wack
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引用次数: 2

Abstract

Tens of millions of shipping containers enter U.S. seaports every year carrying commerce surpassing 1.5 trillion dollars in value. As a result, the maritime shipping industry offers an attractive channel for terrorist organizations to smuggle weapons of mass destruction into the U.S., or to cripple the U.S. economy by directly attacking major ports and maritime infrastructure. In order to prevent such an event from occurring, the Department of Homeland Security has initiated the SAFECON and TRUST programs aimed at improving security measures to detect anomalous goods such as these threats in container air. These programs are working to develop aggressive solutions that minimize any disruption to the flow of commerce by identifying or developing airsample based sensors that can be installed on port gantry cranes or housed within shipping containers themselves. This paper describes the DHS Container Security Test Bed that is being established at the Transportation Security Laboratory to enable realistic evaluation of technologies against real operational challenges. Information highlighting many of these challenges including the concentrations and movement of threat simulants inside containers, background clutter, operational environment, and air sampling capabilities will be presented. This information and the additional data that is being collected at the test bed will allow us to derive sensor and operational requirements and enable the intelligent design and selection of critical technologies.
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对海运集装箱进行大规模杀伤性武器检查
每年有数千万个集装箱进入美国海港,其贸易价值超过1.5万亿美元。因此,海上航运业为恐怖组织向美国走私大规模杀伤性武器,或直接袭击主要港口和海上基础设施,削弱美国经济提供了一个有吸引力的渠道。为了防止此类事件的发生,国土安全部启动了SAFECON和TRUST计划,旨在改进安全措施,以检测集装箱空气中的异常货物,如这些威胁。这些项目正在努力开发积极的解决方案,通过识别或开发可安装在港口龙门起重机上或安装在集装箱内的基于空气样本的传感器,最大限度地减少对商业流动的干扰。本文描述了正在运输安全实验室建立的国土安全部集装箱安全试验台,该试验台能够针对实际操作挑战对技术进行现实评估。将介绍许多突出这些挑战的信息,包括容器内威胁模拟物的浓度和移动、背景杂波、操作环境和空气采样能力。这些信息和在试验台收集的附加数据将使我们能够得出传感器和操作需求,并使关键技术的智能设计和选择成为可能。
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