Seeing the Unseen: Passive RF Sensing via Learning

该项目探讨了无线服务和设备的普及，以提取有关周围环境的有价值的信息。其中，这包括室内环境的占用状态、占用者和移动/运动分类以及可以从无线信号推断的其他高级信息。获得这种及时和准确的态势感知信息的好处是巨大的。实时的占用信息对于智能和绿色建筑至关重要，以减少商业和住宅建筑的碳足迹。准确的运动检测，特别是区分人类和宠物之间的运动的能力，可以帮助提供低成本的家庭安全解决方案。以非侵入性和连续的方式进行自主跌倒检测是为一些最脆弱人群提供长期护理的关键。该项目采用数据驱动的学习方法进行无源RF传感，即，通过对现有射频（RF）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RF损伤、环境波动和收发器位置变化，无源RF感测具有一组独特的挑战，从而导致文献中的不同方法。该项目将无线通信和RF传播领域的专业知识和强大的专业知识带到无源RF传感应用中。这些领域知识对于理解挑战至关重要，并有助于制定相关的学习问题，包括监督降维，数据不平衡学习和采样偏差学习。该项目致力于数据驱动的方法，将建立我们对环境和RF信号接收之间因果关系的认识，并为许多无源RF传感问题提供理论上合理且具有实际意义的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.