EAGER: Ultra-wideband mmWave Radar and Imaging Sensors based on Compressive Sensing

EAGER：基于压缩感知的超宽带毫米波雷达和成像传感器

• 批准号: 0952574
• 负责人: Harish Krishnaswamy
• 金额: $ 5.7万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0952574&HistoricalAwards=false
• 关键词: EAGER; Ultra; wideband; mmWave; Radar

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to investigate novel architectures for the realization of ultra-low-power, integrated, pulse-based, ultra-wideband radar and imaging sensors. The approach is to exploit compressive sensing for high-speed baseband processing to simultaneously achieve high dynamic range while consuming ultra-low power.Ultra-wideband, pulse-based radar and imaging sensors require high-speed baseband processing. Compressive-sensing-based architectures take advantage of the presence of inherent structure in the received radar signal, specifically its time-domain sparsity, to significantly reduce the required sampling rate (and hence, power consumption) while preserving the information contained in the signal. The intellectual merit of the proposed research is to devise and investigate the feasibility of compressive-sensing-based ultra-wideband radar and imaging architectures through theoretical and simulation-based studies of the robustness of compressive sensing to practical issues such as noise, interference, presence of insufficient sparsity, and circuit imperfections such as clock jitter. These studies will be linked to measurements performed with prototype radars constructed with off-the-shelf components.The broader impact of the proposed research is in its ability to transform the current vision of ambient intelligence. The low-power and low-cost nature of silicon-based ultra-wideband radar and imaging sensors utilizing compressive sensing potentially enables ubiquitous deployment of massive sensor networks, which can have a dramatic impact on the quality of day-to-day life. The proposed research also bridges the disciplines of integrated analog- and digital-circuit design, signal processing and applied mathematics. This proposal will support the education and training of a graduate student researcher, who will develop skills in all these fields.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本研究的目标是研究实现超低功耗，集成，基于脉冲的超宽带雷达和成像传感器的新架构。该方法利用压缩感知进行高速基带处理，以在实现高动态范围的同时实现超低功耗。超宽带脉冲雷达和成像传感器需要高速基带处理。基于压缩感测的架构利用接收到的雷达信号中固有结构的存在，特别是其时域稀疏性，以显著降低所需的采样率（并因此降低功耗），同时保留信号中包含的信息。拟议的研究的智力价值是设计和研究基于压缩感知的超宽带雷达和成像架构的可行性，通过理论和基于仿真的研究压缩感知的鲁棒性的实际问题，如噪声，干扰，稀疏性不足的存在，和电路缺陷，如时钟抖动。这些研究将与使用现成组件构建的原型雷达进行的测量相关联。拟议研究的更广泛影响在于其改变当前环境智能愿景的能力。利用压缩感知的硅基超宽带雷达和成像传感器的低功耗和低成本特性可能使大规模传感器网络的无处不在的部署成为可能，这可能对日常生活质量产生巨大影响。拟议的研究还连接了集成模拟和数字电路设计、信号处理和应用数学等学科。该提案将支持研究生研究员的教育和培训，他们将发展所有这些领域的技能。