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的重大关切。探测人体隐藏武器对拯救生命至关重要。目前的检测技术遭受高错误率，无论是积极的还是消极的，即使被扫描的人合作。高成本和低吞吐量阻碍了在学校、体育馆等忙碌的公共建筑中的部署。隐蔽扫描（人们不知道他们正在被扫描）更具挑战性，仍然是一个未解决的问题。然而，这种“隐蔽”系统有时也是充分保护平民所必需的。该项目的重点是开发一种新产品，利用微波雷达对隐藏的武器进行实时扫描和探测。研究将集中在一个软件定义的无线电平台上，该平台在开发、部署甚至运营阶段都具有很大的灵活性。它允许在不改变硬件的情况下进行系统改进，并允许通过安全的网络连接进行更新。这种架构可作为片上系统，这将减少最终用户的尺寸和成本。该系统的硬件占用空间大约是笔记本电脑的大小。由于辐射是非电离的和低功耗的，因此检测系统是安全的。另一个优点是隐藏的物体将在不生成身体图像的情况下被检测到，从而避免了隐私问题。该产品的一个显著特征是其持续了解部署地点、新威胁（例如武器）和新的非威胁（例如手机）的认知能力。识别是由机器学习分类器，这是“训练”通过应用统计分析，以大套雷达响应威胁，非威胁，和背景环境。分类主要取决于这些测量，特别是部署地点的测量。因此，该产品在安装后将不断获取新的测量值，以提高检测成功率。新的威胁/非威胁数据以及新的分类器也将通过安全的网络更新提供。

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.