Optimal Layered Resource Management and Data Processing for Threat Detection in Urban Environments

城市环境中威胁检测的最佳分层资源管理和数据处理

• 批准号: 538404-2018
• 负责人: Kirubarajan, Thia
• 金额: $ 6.99万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699500
• 关键词: Optimal; Layered; Resource; Management; Data

Canada must be able to secure and protect the people and infrastructure in its urban environments. To do so, threats such as terrorist events and improvised explosive devices, in urban environments need to be identified early not only to safeguard the citizens and the infrastructure, but also to maintain the citizens' confidence in law and order and their trust in the government. In the case of threats that are mobile, it is also essential to accurately track and predict their kinematic states as they evolve over space and time. To detect such stationary or kinematic threats, sensors or signal sources such as cameras, radars, lidars, communication devices, radio and television stations, and cell phone towers can be used in an urban environment. In some urban scenarios, there might be an abundance of sensors due to ubiquitous cameras and cell phones, which could overload the communication and computational resources, or a dearth of accessible sensors due to the presence of buildings that block signal propagation, which would adversely affect the quality of threat detection and tracking. In either case, it is essential to strike a balance between (i) sensing, communication and computational resource utilization and (ii) the desired timeliness and accuracy of threat detection and tracking. This need to balance motivates the proposed research work to develop algorithms for optimal management of sensor, communication and computational resources as well as algorithms for threat detection and object tracking in urban environments using sensors and processors mounted on stationary or moving platforms. The proposed work will train a number of highly qualified personnel (HQP) in sensor and data processing with application to safety and security. In addition, the trained HQP can be employed and the resulting algorithms deployed in other fields such as autonomous vehicles, intelligent transportation and smart cities, which are of importance to maintain Canada's edge in today's high-tech world.

加拿大必须能够在其城市环境中保护和保护人民和基础设施。为此，在城市环境中，诸如恐怖事件和简易爆炸装置之类的威胁不仅需要尽早确定以维护公民和基础设施，还需要维持公民对法律和秩序的信心以及他们对政府的信任。对于移动的威胁，随着时间和时间的发展，准确跟踪和预测其运动学状态也是必不可少的。为了检测这种固定或运动学威胁，传感器或信号源，例如相机，雷达，激光雷达，通信设备，广播和电视台以及手机塔可以在城市环境中使用。 在某些城市场景中，由于无处不在的摄像机和手机，可能会有大量的传感器，这可能会超载通信和计算资源，或者由于存在阻碍信号传播的建筑物而缺乏可访问的传感器，这会对威胁检测和跟踪的质量产生不利影响。无论哪种情况，都必须在（i）传感，通信和计算资源利用以及（ii）威胁检测和跟踪的所需及时性和准确性之间取得平衡。这种需要平衡的需要激发拟议的研究工作，以开发算法，以最佳管理传感器，通信和计算资源，以及使用安装在固定或移动平台上的传感器和处理器在城市环境中进行威胁检测和对象跟踪的算法。拟议的工作将在传感器和数据处理中培训许多高素质的人员（HQP），并应​​用于安全和保障。此外，可以使用训练有素的HQP，并在其他领域（例如自动驾驶汽车，智能运输和智能城市）中部署的算法，这对于在当今高科技世界中保持加拿大的优势非常重要。