Advanced Radio-Frequency(RF) Based Environmental Monitoring Systems

时间衰落是指发射器和接收器之间的无线电信道的变化，例如，笔记本电脑和接入点之间的信号强度波动，即使两者都不移动。过去对时间衰落的研究仅将其视为降低无线通信的问题。新出现的研究表明，可以利用时间衰落来定位、自动识别活动或手势，并监控无线网络附近的人的健康状况。这些定位、识别和监控系统称为基于射频的环境监控(REM)系统。快速眼动技术的改进可能有助于警察和搜救系统的设计，这些系统可以在危险或倒塌的建筑物中定位呼吸的人。作为另一个例子，部署在家庭中的REM技术可以检测跌倒，并检测认知或身体衰退的迹象，作为就地老化传感器系统的一部分。REM技术可以让人们通过放在床边的无线设备(例如手机)来诊断睡眠障碍。最后，REM系统可能会给室内和室外安全系统带来革命性的变化，帮助保护用现有技术难以监控的区域和建筑物。到目前为止，还没有对时间衰落机制的基础研究来支持REM应用。这个项目的研究考虑了时间衰落，并试图确定它如何受到环境中人的移动的影响，以便将其用于环境监测。该项目将开发、验证和开发新的模型，用于接收到的功率和通道响应发生的时间变化。范围既包括大规模运动，即一个人穿过房间，也包括小规模运动，即一个人呼吸，或移动手臂。众所周知，运动的影响作为信道的其他多径衰落特性的函数并且作为人相对于发射机和接收机的位置的函数而变化，但不知道是如何变化的。这个项目将用统计模型来解释这些关系，这些模型描述了由于人和物体的运动、呼吸和其他周期性运动而引起的时间衰落变化，作为人或物体的位置的函数以及其他多径通道特性的函数。所有模型都将使用真实世界的测量、在电波暗室中的受控测量和数字电磁模拟进行验证。该项目将从他们那里开发改进的估计器，确定改进的学习方法，并减少部署基于射频的环境监测系统所需的手动培训。各种无设备定位(DFL)和活动识别算法，以及手势识别和呼吸监测算法，都可以在准确性和效率方面进行改进。例如，基于指纹的DFL方法可以用更少的训练测量来实现同样的工作，统计倒置DFL方法可以实现更高的精度，并且基于RF的呼吸监测系统可以变得更健壮。

Temporal fading is the change in the radio channel between a transmitter and receiver, for example, a fluctuating "number of bars" or signal strength between a laptop and access point, even when neither are moving. Past research in temporal fading treats it only as a problem that degrades wireless communication. Emerging research has shown that temporal fading can be exploited to locate, automatically recognize the activity or gesture, and monitor the health of people in the vicinity of a wireless network. These localization, recognition, and monitoring systems are called RF-based environment monitoring (REM) systems. Improvements in REM technologies could aid in the design of police and search-and-rescue systems that locate breathing people in dangerous or collapsed buildings. As another example, REM technologies deployed in a home could detect falls and detect signs of cognitive or physical decline as part of an aging-in-place sensor system. REM technologies could allow people to diagnose disordered sleeping via wireless devices (e.g., cell phones) left on their bedside. Finally, REM systems could revolutionize indoor and outdoor security systems, helping to protect areas and buildings which are difficult to monitor with existing technologies. To date, no fundamental research in temporal fading mechanisms has been performed to support REM applications. Research in this project considers temporal fading and seeks to establish how it is affected by the movements of people in the environment so that it can be exploited for environmental monitoring.This project will develop, verify, and exploit new models for the temporal changes that occur to received power and channel response. The scope includes both large-scale motion, i.e., a person walking across a room, and small-scale motion, i.e., a person breathing, or moving an arm. It is known that the effects of motion vary as a function of other multipath fading characteristics of the channel and as a function of the person's position with respect to the transmitter and receiver, but it is not known how. This project will explain these relationships with statistical models that describe temporal fading changes due to human and object motion, to breathing and other periodic motion, as a function of the position of the person or object and a function of other multipath channel characteristics. All models will be verified using real-world measurements, using controlled measurements in an anechoic chamber, and using numerical electromagnetic simulation. From them, the project will develop improved estimators, identify improved features for learning methods, and reduce the manual training required to deploy RF-based environment monitoring systems. A wide variety of device-free localization (DFL) and activity recognition algorithms, as well as gesture recognition and breathing monitoring algorithms, can be improved both in terms of accuracy and efficiency. For example, fingerprint-based DFL methods can be enabled to work as well with fewer training measurements, statistical inversion DFL methods can achieve improved accuracy, and RF-based breathing monitoring systems can be made more robust.