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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=692578
• 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.

加拿大必须能够确保和保护其城市环境中的人民和基础设施。为此，需要及早发现城市环境中的恐怖主义事件和简易爆炸装置等威胁，这不仅是为了保护公民和基础设施，也是为了维持公民对法律和秩序的信心以及对政府的信任。在威胁是移动的的情况下，还必须准确地跟踪和预测其随空间和时间演变的运动状态。为了检测这种静止或运动的威胁，可以在城市环境中使用传感器或信号源，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。在某些城市场景中，由于无处不在的相机和手机，可能会有大量的传感器，这可能会使通信和计算资源过载，或者由于存在阻挡信号传播的建筑物而缺乏可访问的传感器，这将不利地影响威胁检测和跟踪的质量。在任何一种情况下，都必须在（i）传感，通信和计算资源利用与（ii）威胁检测和跟踪的所需及时性和准确性之间取得平衡。这种平衡的需要促使拟议的研究工作，以开发算法的传感器，通信和计算资源的最佳管理，以及算法的威胁检测和目标跟踪在城市环境中使用传感器和处理器安装在固定或移动平台。拟议的工作将在传感器和数据处理方面培训一些高素质的人员（HQP），并将其应用于安全和安保。此外，经过训练的HQP可以用于其他领域，如自动驾驶汽车，智能交通和智能城市，这对于保持加拿大在当今高科技世界的优势至关重要。