Overview of portal monitoring at border crossings
Overview of portal monitoring at border crossings
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DOI：
10.1109/nssmic.2003.1352095
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发表时间：
2003-10
期刊：
影响因子：
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通讯作者：
B. Geelhood;J. Ely;R. Hansen;R. Kouzes;J. Schweppe;R. Warner
中科院分区：
文献类型：
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作者：
B. Geelhood;J. Ely;R. Hansen;R. Kouzes;J. Schweppe;R. Warner
海关和边境保护局的任务是在入境港拦截非法放射性材料。令人关切的项目包括辐射扩散装置、核弹头和特殊核材料。首选的调查方法筛选所有主要车辆，并将可疑车辆转移到次要车辆。这就要求在一级有很高的探测概率，而二级警报不会压倒一切，这可能包括在可接受的货物中发现的天然放射性物质和医疗程序中使用的放射性核素。灵敏的报警算法必须适应每当车辆进入入口时观察到的基线抑郁。基于能量的算法可以有效地使用从塑料闪烁体获得的原始能量信息来区分NORM和SNM。每当NORM货物限制报警阈值，基于能量的算法产生显着更好的检测概率小SNM源比总计数算法。可以使用大的经验数据集来最好地评估算法，以1）计算虚警概率，2）选择操作上可接受的虚警率的σ水平阈值，以及3）确定SNM的边缘可检测伪源的检测概率。
The Bureau of Customs and Border Protection has the task of interdicting illicit radioactive material at ports of entry. Items of concern include radiation dispersal devices (RDD), nuclear warheads, and special nuclear material (SNM). The preferred survey method screens all vehicles in primary and diverts questionable vehicles to secondary. This requires high detection probability in primary while not overwhelming secondary with alarms, which could include naturally occurring radioactive material (NORM) found in acceptable cargo and radionuclides used in medical procedures. Sensitive alarm algorithms must accommodate the baseline depression observed whenever a vehicle enters the portal. Energy-based algorithms can effectively use the crude energy information available from a plastic scintillator to distinguish NORM from SNM. Whenever NORM cargo limits the alarm threshold, energy-based algorithms produce significantly better detection probabilities for small SNM sources than gross-count algorithms. Algorithms can be best evaluated using a large empirical data set to 1) calculate false alarm probabilities, 2) select sigma-level thresholds for operationally acceptable false alarm rates, and 3) determine detection probabilities for marginally detectable pseudo sources of SNM.