Advanced Radio-Frequency(RF) Based Environmental Monitoring Systems

基于先进射频 (RF) 的环境监测系统

• 批准号: 1407949
• 负责人: Neal Patwari
• 金额: $ 18.77万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407949&HistoricalAwards=false
• 关键词: Advanced; Radio; Frequency; RF; Based

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.

时间衰落是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波动的“条数”或信号强度，即使两者都不移动。 过去的研究在时间衰落处理它只是作为一个问题，降低无线通信。 新兴的研究表明，时间衰落可以用来定位，自动识别活动或手势，并监测无线网络附近的人的健康状况。 这些定位、识别和监视系统被称为基于RF的环境监视（REM）系统。 快速眼动技术的改进可以帮助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找到呼吸的人。 作为另一个例子，部署在家中的REM技术可以检测福尔斯，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无线设备诊断睡眠障碍（例如，手机）放在床边。 最后，REM系统可以彻底改变室内和室外安全系统，帮助保护现有技术难以监控的区域和建筑物。 到目前为止，还没有进行基本的研究，在时间衰落机制，以支持REM应用。 本项目的研究考虑了时间衰落，并试图确定它是如何受到环境中的人的运动，以便它可以用于环境监测的影响。本项目将开发，验证和开发新的模型，发生的时间变化的接收功率和信道响应。 范围包括大尺度运动，即，一个人在房间里走动，以及小规模的运动，即，已知运动的影响作为信道的其它多径衰落特性的函数和作为人相对于发射机和接收机的位置的函数而变化，但不知道如何变化。 这个项目将解释这些关系与统计模型，描述由于人和物体的运动，呼吸和其他周期性运动的时间衰落变化，作为一个功能的人或物体的位置和其他多径信道特性的函数。 所有模型将使用真实世界的测量，使用控制测量在消声室，并使用数值电磁模拟进行验证。 通过这些，该项目将开发改进的估计器，确定学习方法的改进功能，并减少部署基于RF的环境监测系统所需的手动培训。各种各样的无设备定位（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得到改进。 例如，可以使得基于指纹的DFL方法能够在较少的训练测量的情况下同样工作，统计反演DFL方法可以实现改进的准确度，并且可以使基于RF的呼吸监测系统更鲁棒。