课题基金 / 基金详情

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

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:
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
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31

项目摘要

项目成果

Kirubarajan, Thia的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Robust State Estimation in Uncertain Environments Using Point Process Models
  • 批准号:
    RGPIN-2017-05365
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $4.23万
  • 财政年份:
    2021
  • 负责人:
    Kirubarajan, Thia
  • 依托单位:
Airborne Tracking of Small Ground and Maritime Targets Under Realistic Conditions
  • 批准号:
    535810-2018
  • 项目类别:
    Collaborative Research and Development Grants
  • 资助金额:
    $26.23万
  • 财政年份:
    2021
  • 负责人:
    Kirubarajan, Thia
  • 依托单位:
Optimal Layered Resource Management and Data Processing for Threat Detection in Urban Environments
  • 批准号:
    538404-2018
  • 项目类别:
    Collaborative Research and Development Grants
  • 资助金额:
    $6.99万
  • 财政年份:
    2021
  • 负责人:
    Kirubarajan, Thia
  • 依托单位:
Optimal Layered Resource Management and Data Processing for Threat Detection in Urban Environments
  • 批准号:
    538404-2018
  • 项目类别:
    Collaborative Research and Development Grants
  • 资助金额:
    $6.99万
  • 财政年份:
    2020
  • 负责人:
    Kirubarajan, Thia
  • 依托单位:
海外基金