Statistical Algorithms for Threat Detection via Sensor Networks

威胁检测（TD）是指对有害物质（生物、化学或核）的存在进行评估。这些代理人的足迹（或签名）可以是定性的、口头的和/或定量的。这些签名是由传感器产生的，这些传感器共同构成了一个网络系统。这种系统的架构可以是串联的、并行的或分层的。这种签名的一个关键特征是它们往往不精确、不完整和不可靠。此外，传感器可以是合作的，也可以是对抗的，后者是由于破坏和心理策略。首席研究员和他的同事们建议阐明TD场景的数学基础，以便以一种有原则的方式整合来自众多传感器的签名。目标是用数值概率来表达威胁的存在。这相当于集成通过分布式网络结构过滤的、受不精确、伪装和谈判污染的签名。作为概率论和统计学领域的一个研究课题，污染与伪装信号的集成是一个比较新的课题。将调用贝叶斯方法和经典方法，以及两者的巧妙组合。这项工作的关键将需要开发有意义的可能性函数，以捕捉生理和心理问题的本质。目前在情报和国家安全方面的做法是用口头和定性的术语来表达威胁，比如可能的，可能的，可能的等等。这样的表达是不可起诉的。本研究将威胁检测场景置于概率框架中，以便采取果断行动来减轻威胁。这项工作将对民用应用产生更广泛的影响，如石油勘探、天气预报、医疗诊断和社会文化建模。

Threat detection (TD) means an assessment of the presence of harmful agents, biological, chemical, or nuclear. The footprint (or signatures) of such agents could be qualitative and verbal, and/or quantitative. These signatures are generated by sensors which collectively form a networked system. The architecture of such systems could be series, parallel, or hierarchical. A key feature of such signatures is that they tend to be imprecise, incomplete, and unreliable. Furthermore, the sensors could be co-operative or adversarial, the latter due to sabotage and psychological ploys. The principal investigator and his colleagues propose to articulate the mathematical underpinnings of the TD scenario, in order to integrate signatures from a multitude of sensors in a principled way. The goal is to express the presence of threats in terms of numerical probabilities. This tantamounts to integrating signatures which are filtered via distributed network structures, and are contaminated by imprecision, camouflage, and parleying. As a research topic in probability and statistics, the matter of integrating contaminated and camouflaged signatures is new. Both Bayesian and classical methods, as well as a cunning combination of the two, will be invoked. The crux of the work will entail developing meaningful likelihood functions that capture the essence of the physical and psychological issues.Current practice in intelligence and national security is to express threat in verbal and qualitative terms like possible, probable, likely, etc. Such expressions are not actionable. This research will place the threat detection scenario in a probabilistic framework so that decisive actions to mitigate threats can be taken. The work will have broader impacts in civilian applications such as oil exploration, weather prediction, medical diagnosis, and socio-cultural modeling.