Seeing the Unseen: Passive RF Sensing via Learning

看到看不见的东西：通过学习进行无源射频传感

• 批准号: 2036236
• 负责人: Biao Chen
• 金额: $ 23.5万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036236&HistoricalAwards=false
• 关键词: Seeing; Unseen; Passive; RF; Sensing

This project explores the prevalence of wireless services and devices to extract valuable information about the ambient environment. This includes, among others, occupancy status of an indoor environment, occupant and movement/motion classification, and other high-level information that can be inferred from wireless signals. The benefits of having access to such timely and accurate situational awareness information are enormous. Real-time occupancy information is essential for intelligent and green building to reduce carbon footprint of commercial and residential buildings. Accurate motion detection, and in particular, the ability to distinguish motions between human and pets can help provide low-cost home security solutions. Autonomous fall detection in a non-intrusive and continuous manner is key to providing long-term care for the well-being of some of the most vulnerable populations.This project takes a data-driven learning approach for passive RF sensing, i.e., extracting situational awareness information of the ambient environment through judicious processing of existing radio frequency (RF) signals. Passive RF sensing has a unique set of challenges due to inherent RF impairments, environment fluctuation, and transceiver location changes, leading to divergent approaches in the literature. This project brings domain knowledge and strong expertise in wireless communication and RF propagation to passive RF sensing applications. Such domain knowledge is critical for understanding the challenges and helps inform the formulation of associated learning problems including supervised dimensionality reduction, learning with data imbalance, and learning with sampling bias. Striving for a data-driven approach, the project will build up our knowledge of the cause-and-effect relationship between environment and RF signal reception and provide theoretically sound and practically meaningful solutions to a number of passive RF sensing problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目探索无线服务和设备的普及性，以提取有关周围环境的有价值的信息。这包括室内环境的占用状态、占用者和运动/运动分类以及可以从无线信号推断的其他高级信息等。能够获得这种及时和准确的态势感知信息的好处是巨大的。实时的入住率信息是智能绿色建筑减少商住建筑碳足迹的关键。准确的运动检测，特别是区分人类和宠物运动的能力，可以帮助提供低成本的家庭安全解决方案。非侵入性和持续的自主跌倒检测是为一些最脆弱的人群提供长期护理的关键。本项目采用数据驱动的学习方法进行被动射频感知，即通过对现有射频信号的明智处理来提取周围环境的态势感知信息。由于固有的射频损伤、环境波动和收发信机位置变化，被动射频侦听面临着一系列独特的挑战，导致文献中采用的方法各不相同。该项目将无线通信和射频传播方面的领域知识和强大的专业知识带到无源射频侦听应用中。这样的领域知识对于理解挑战至关重要，并有助于为相关学习问题的形成提供信息，包括监督降维、数据不平衡学习和采样偏差学习。致力于数据驱动的方法，该项目将加深我们对环境和射频信号接收之间的因果关系的了解，并为许多无源射频传感问题提供理论上合理和实际有意义的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。