Signal Processing for Passive RF Sensing

无源射频传感的信号处理

• 批准号: 1609393
• 负责人: Hongbin Li
• 金额: $ 33万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609393&HistoricalAwards=false
• 关键词: Signal; Processing; Passive; RF; Sensing

This project considers passive radio frequency (RF) sensing that employs wireless communication signals as illuminators of opportunities (IOs) to detect, locate, and track objects of interest. Such capabilities are useful for a wide range of applications, e.g., indoor localization, health monitoring, vehicle tracking, and many more. Originally considered as a supplementary technology to active sensing (e.g., radar), passive RF sensing has emerged as a promising alternative that far exceeds the scope of standard radar operations. Passive RF sensing has many advantages over its active counterpart: no dedicated transmitter and RF pollution free; covert operation; order of magnitude cheaper to build, deploy and operate; and the ability to simultaneously access several IOs to obtain multiple views and spatial diversity of the surveillance area. Recent interest in passive RF sensing is further driven by spectrum scarcity and a growing pressure from the wireless sector to release government-held spectrum, including spectrum allocated to active sensing, in order to boost the economy. Given the ubiquity of wireless infrastructure, it is expected that passive RF sensing will be integrated in people's daily life. For example, wireless service providers can offer wireless devices with built-in passive RF sensing functions and make them broadly available to any user. Thus, the associated economical and societal impact of the proposed research is substantial.Despite the advantages, there are fundamental technical issues that need to be investigated for passive RF sensing. Specifically, unlike active sensing which has a well-established theory for optimum signal processing based on the matched filter (MF), existing passive sensing techniques were introduced largely by imitating the MF, without adequately considering the differences between the two systems. The MF requires knowledge of the transmitted waveform that is unavailable in a passive system due to the non-cooperative nature of the IO. The standard strategy is to replace it with a reference signal, which is a noisy copy of the IO waveform obtained via an antenna steered toward the IO, and cross-correlates (CC) the reference signal with the received signal. The CC detector is not an optimal method. In fact, it is very sensitive to the noise contained in the reference and further deteriorated by the direct-path interference, i.e., the direct transmission from the IO to the passive receiver. To address these issues, this project aims to develop novel signal processing techniques for passive RF sensing by taking into account impairments such as noisy reference, DPI, and multi-path clutter, which are inherent in passive systems. In addition, the project will develop techniques for passive RF sensing involving sensors placed on moving platforms (e.g., unmanned aerial vehicles) to obtain close-up looks of the scene, which is of interest for disaster relief, rural search, and many other applications. A major technical challenge there is to cope with excessive clutter Doppler spread induced by platform motion. Finally, this project also has a significant educational component aimed to provide integrated research experience and training for undergraduate and graduate students.

该项目认为采用无线通信信号作为机会（iOS）来检测，定位和跟踪感兴趣的对象的无线通信信号（RF）感应。此类功能可用于广泛的应用，例如室内定位，健康监测，车辆跟踪等等。最初被认为是主动传感的补充技术（例如雷达），被动RF传感已成为一种有希望的替代方案，远远超过了标准雷达操作的范围。被动RF感应比其主动对应物具有许多优势：没有专用发射器和RF污染；秘密操作；构建，部署和操作便宜的数量级；以及同时访问多个iOS以获得监视区域的多个视图和空间多样性的能力。最近，对被动RF感知的兴趣进一步驱动了频谱稀缺性以及无线部门对释放政府控制的频谱的越来越大的压力，包括分配给主动感应的频谱，以促进经济。鉴于无线基础设施无处不在，预计被动RF感应将集成到人们的日常生活中。例如，无线服务提供商可以提供具有内置无源RF传感功能的无线设备，并使它们可为任何用户提供广泛的可用性。因此，拟议研究的相关经济和社会影响是很大的。尽管有优势，但仍需要研究基本的技术问题以进行被动RF感应。具体而言，与主动传感不同，它具有基于匹配过滤器（MF）的最佳信号处理的良好理论，而现有的被动传感技术主要是通过模仿MF来引入的，而没有充分考虑两个系统之间的差异。 MF需要了解由于IO的不合作性质而在被动系统中无法使用的传输波形的知识。标准策略是用参考信号替换它，这是通过转向IO的天线获得的IO波形的嘈杂副本，并互相关（CC）带有接收信号的参考信号。 CC检测器不是最佳方法。实际上，它对参考中包含的噪声非常敏感，并因直接路径干扰而进一步恶化，即从IO到被动接收器的直接传输。为了解决这些问题，该项目旨在通过考虑诸如被动系统固有的噪声参考，DPI和多路径混乱之类的障碍来开发用于被动RF感应的新型信号处理技术。此外，该项目将开发用于被动RF传感的技术，涉及放置在移动平台（例如，无人驾驶飞机）上的传感器，以获得现场的特写外观，这对救灾，乡村搜索和许多其他应用程序都很感兴趣。一个重大的技术挑战是应对由平台运动引起的过多的混乱多普勒传播。最后，该项目还具有重要的教育部分，旨在为本科生和研究生提供综合的研究经验和培训。