Signal Processing for Passive RF Sensing

该项目考虑无源射频（RF）传感，利用无线通信信号作为机会照明器（IOs）来检测、定位和跟踪感兴趣的对象。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。最初被认为是主动传感（例如雷达）的补充技术，无源射频传感已经成为一种有前途的替代方案，远远超出了标准雷达操作的范围。无源射频传感与有源射频传感相比有许多优点：没有专用发射机，射频无污染；秘密行动;建造、部署和运营成本降低了一个数量级；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。频谱稀缺和无线行业要求释放政府持有的频谱（包括分配给主动传感的频谱）以促进经济增长的日益增长的压力，进一步推动了人们对无源射频传感的兴趣。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提供内置无源射频传感功能的无线设备，并使其广泛适用于任何用户。因此，拟议研究的相关经济和社会影响是实质性的。尽管有这些优势，但无源射频传感仍有一些基本的技术问题需要研究。具体来说，与基于匹配滤波器（MF）的优化信号处理的完善理论的主动传感不同，现有的被动传感技术主要是通过模仿MF来引入的，而没有充分考虑两种系统之间的差异。由于IO的非合作性质，MF需要了解传输波形，这在无源系统中是不可用的。标准策略是用参考信号代替它，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交叉相关（CC）。CC检测器不是最优的方法。实际上，它对参考信号中包含的噪声非常敏感，并且由于直接路径干扰（即从IO直接传输到无源接收器）而进一步恶化。为了解决这些问题，本项目旨在通过考虑无源系统固有的噪声参考、DPI和多路径杂波等缺陷，开发用于无源射频传感的新型信号处理技术。此外，该项目将开发无源射频传感技术，包括放置在移动平台（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、农村搜索和许多其他应用很感兴趣。一个主要的技术挑战是如何处理由平台运动引起的过多的杂波多普勒扩散。最后，这个项目也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的研究经验和培训。

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.