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.
