Cognitive microwave radar for the stand-off detection of on-body concealed weapons - phase IIb (partnership with a canadian company)

犯罪和恐怖主义行为是现代社会非常关注的问题。发现身上隐藏的武器对拯救生命至关重要。目前的检测技术存在很高的错误率，无论是阳性还是阴性，即使被扫描的人合作。高成本和低吞吐量阻碍了繁忙的公共建筑如学校、体育场等的部署。隐蔽扫描，人们不知道他们正在被扫描，是更具挑战性的，仍然是一个未解决的问题。然而，为了充分保护平民，有时需要这样的“隐蔽”系统。该项目的重点是开发一种使用微波雷达实时扫描和探测隐藏武器的新产品。研究将集中在软件定义的无线电平台上，该平台在开发、部署甚至操作阶段提供极大的灵活性。它允许在不改变硬件的情况下进行系统改进，并通过安全的网络连接进行更新。这种架构可以作为芯片上的系统使用，这将减少最终用户的尺寸和成本。该系统的硬件占地面积大约相当于一台笔记本电脑的大小。该检测系统是安全的，因为辐射是非电离的，低功耗。另一个优点是，隐藏的物体不会被检测到，而不会产生人体图像，从而避免了隐私问题。该产品的一个显著特征是其持续学习部署地点、新威胁（例如武器）和新的非威胁（例如手机）的认知能力。识别是通过机器学习分类器实现的，机器学习分类器通过对威胁、非威胁和背景环境的大量雷达响应应用统计分析来“训练”。分类在很大程度上取决于这些度量，特别是部署站点的度量。因此，该产品在安装后将继续获取新的测量值，以提高检测成功率。新的威胁/非威胁数据，以及新分类器，也将通过安全网络更新提供。

Crime and acts of terrorism are of great concern in modern society. The detection of on-body hidden weaponsis of paramount importance for saving lives. Current detection technologies suffer from high false rates, bothpositive and negative, even if the scanned people cooperate. High cost and low throughput prevent deploymentin busy public buildings such as schools, stadiums, etc. Covert scanning, where people are unaware that theyare being scanned, is even more challenging and remains an unsolved problem. Yet such "covert" systems aresometimes needed to adequately protect civilians.This project focuses on the development of a new product for real-time scanning and detection of concealedweapons using microwave radar. Research will focus on a software-defined radio platform that offers greatflexibility during development, deployment and even operational phases. It allows for system improvementswithout hardware changes and for updates over secure network connections. This architecture is available as asystem on a chip, which will reduce the size and the cost to the end-user. The system's hardware footprint willbe roughly the size of a notebook.The detection system is safe as the radiation is non-ionizing and low-power. Another advantage is that theconcealed objects will be detected without generating an image of the body, thus avoiding privacy concerns.A distinct feature of this product is its cognitive ability to continuously learn about the deployment site, aboutnew threats (e.g. weapons), and about new non-threats (e.g. cell phones). Recognition is enabled by machinelearning classifiers, which are "trained" by applying statistical analysis to large sets of radar responses tothreats, non-threats, and background environments. Classification depends critically on these measurements,particularly those of the deployment site. Therefore, the product will continue to acquire new measurementsafter installation in order to improve the detection success. New threat/non-threat data, as well as novelclassifiers, will also be provided via secure network updates.