Signal Processing for Passive RF Sensing

该项目考虑使用无线通信信号作为机会照明器(IO)来检测、定位和跟踪感兴趣的对象的无源射频(RF)感知。这些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。无源射频传感最初被认为是有源传感(如雷达)的一项补充技术，现已成为一种前景广阔的替代方案，远远超出了标准雷达作战的范围。与主动射频侦听相比，被动射频侦测具有许多优势：没有专用发射器和射频无污染；隐蔽操作；建造、部署和操作成本低数量级；能够同时访问多个IOS，以获得监视区域的多个视图和空间多样性。最近对无源射频传感的兴趣进一步受到频谱稀缺和无线部门越来越大的压力的推动，这些压力要求释放政府持有的频谱，包括分配给主动传感的频谱，以提振经济。鉴于无线基础设施的无处不在，无源射频传感有望融入人们的日常生活。例如，无线服务提供商可以为无线设备提供内置的无源射频侦听功能，并使其广泛适用于任何用户。因此，这项研究的相关经济和社会影响是巨大的。尽管有这些优点，但仍有一些基本的技术问题需要研究。具体地说，与基于匹配滤波器(MF)的最优信号处理理论的有源传感不同，现有的被动传感技术主要是通过模仿匹配滤波器来引入的，没有充分考虑两个系统之间的差异。MF需要所传输的波形的知识，这在无源系统中由于IO的非合作性质而不可用。标准策略是用参考信号替换它，参考信号是通过指向IO的天线获得的IO波形的噪声副本，并将参考信号与接收信号互相关(CC)。CC检测器不是一种最佳的方法。事实上，它对参考中包含的噪声非常敏感，并因直接路径干扰(即从IO到无源接收器的直接传输)而进一步恶化。为了解决这些问题，本项目旨在开发新的信号处理技术，通过考虑无源系统固有的噪声参考、DPI和多径杂波等损害，来实现无源射频侦听。此外，该项目将开发被动射频传感技术，涉及放置在移动平台(例如无人驾驶飞行器)上的传感器，以获得现场的近距离观察，这对救灾、乡村搜索和许多其他应用很有意义。一个主要的技术挑战是处理平台运动引起的过多的杂波多普勒扩展。最后，该项目还有一个重要的教育组成部分，旨在为本科生和研究生提供综合研究经验和培训。

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.