Leveraging Bandwidth-Rich Wireless Signals for Passive Localization of RF-Silent Mobile Objects

可以利用许多无线通信信号、WiFi、蜂窝、无线电/TV和卫星来提供作为副产品的无源射频（RF）感测服务，包括对象和事件检测、定位、健康护理监视等。无源RF感测能够定位非合作无设备对象，其通常比配备有有源RF设备的合作对象（例如，移动的电话）。然而，大多数现有的被动解决方案是基于非相干处理的，使用信号强度或信道状态信息。非相干方法由于其固有的物理限制而具有分辨率差、覆盖范围小和性能波动的缺点。它们在利用较新的无线信号的较大带宽（BW）方面是无效的。为了超越最先进的非相干方法的局限性，该项目将开发相干高分辨率技术，通过利用5G/6 G蜂窝、WiFi-6/7等带宽丰富的无线信号，在复杂的室内和室外多径环境中定位无源射频静默移动的物体。无源RF感应无需专用发射器，是环保绿色。它是非侵入性和经济的，不需要额外的基础设施投资。该项目的研究成果可以与现有的无线网络集成，使服务提供商能够为客户提供无线通信和RF传感服务。因此，拟议研究的潜在经济和社会影响可能是巨大的。在教育方面，该项目将提供培训本科生和研究生的机会，针对当地高中生的暑期研究计划，以及PI与妇女，少数民族和来自代表性不足的群体的学生的合作。该项目的目标是开发一个基于相干处理的框架，用于多径环境中多个目标的鲁棒高分辨率定位和映射。它包括三个研究重点。推力A的重点是被动定位和映射技术的发展，采用射频发射从多个无线基站（BS），形成一个多静态的传感几何形状，并提供多视角的监视区域。一项任务是开发一种直接映射方法，该方法直接从观测信号中产生目标的位置和速度估计，从而绕过传统间接方法中涉及的组合数据关联步骤。其他的研究任务包括开发抗多径定位方法，通过利用多静态传感系统中固有的空间和几何多样性，以及多速率快/慢时间采样方法，该方法可以实现解耦的位置和速度估计，以降低复杂性。虽然Thrust A主要考虑可以访问所有BS的测量的集中式方法，但Thrust B扩展了努力并为所提出的多静态无源传感系统开发了分布式隐私保护定位和映射算法。其目的是寻求鲁棒性，计算效率和隐私。后者是由于多静态系统涉及为具有潜在私有数据的不同通信用户服务的不同BS而出现的。所提出的算法在每个BS并行运行，共享中间计算结果而不是原始数据，以保护数据隐私，并通过新颖的图划分技术产生最小的通信开销。Thrust C致力于基于软件定义无线电（SDR）的测试平台，用于实验数据收集和测试，以及评估用于消除杂波和直接路径干扰的数据清理方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many wireless communication signals, WiFi, cellular, radio/TV, and satellites, can be utilized to provide passive radio frequency (RF) sensing services as by-products, including object and event detection, localization, healthcare monitoring, etc. Passive RF sensing is capable of locating non-cooperative device-free objects, which are usually harder to locate than cooperative objects equipped with an active RF device (e.g., a mobile phone). However, most prior passive solutions are non-coherent processing based, using signal strength or channel state information. Non-coherent methods suffer poor resolution, small coverage, and fluctuating performance due to their intrinsic physical limitations. They are ineffective in exploiting the larger bandwidth (BW) of newer wireless signals. Aimed to surpass the limitations of state-of-the-art non-coherent methods, this project will develop coherent high-resolution techniques to locate passive RF-silent mobile objects in complex indoor and outdoor multipath environments, by harnessing bandwidth-rich wireless signals such as 5G/6G cellular, WiFi-6/7, among others. Passive RF sensing, which obviates the need for dedicated transmitters, is environmentally green. It is nonintrusive and economical, requiring no additional investment in infrastructure. Research outcomes of this project can potentially be integrated with existing wireless networks, enabling service providers to offer both wireless communication and RF sensing services to their customers. Therefore, the potential economic and societal impact of the proposed research can be substantial. On the educational front, this project will offer opportunities for training undergraduate and graduate students, summer research programs targeting local high school students, and engagement of the PI to work with women, minorities, and students from under-represented groups.The objective of this project is to develop a coherent processing based framework for robust high-resolution localization and mapping of multiple targets in multipath environments. It comprises three research thrusts. Thrust A is focused on the development of passive localization and mapping techniques by employing RF emissions from multiple wireless base stations (BSs), which form a multi-static sensing geometry and provide multi-perspectives of the surveillance area. One task is to develop a direct mapping approach, which yields targets' location and velocity estimates directly from the observed signals, thus bypassing a combinatorial data association step involved in conventional indirect methods. Other research tasks include the development of multipath-resistant localization methods, by capitalizing on spatial and geometric diversity inherent in the multi-static sensing system, and a multi-rate fast/slow-time sampling approach, which enables decoupled location and velocity estimation to reduce the complexity. While Thrust A mainly considers centralized methods that have access to all BSs' measurements, Thrust B extends the effort and develops distributed privacy-preserving localization and mapping algorithms for the proposed multi-static passive sensing system. The purpose is to seek robustness, computational efficiency, and privacy. The latter arises as the multi-static system involves different BSs serving different communication users with potentially private data. The proposed algorithms run in parallel at each BS, which shares intermediate computing results instead of raw data to preserve data privacy, and incur minimum communication overhead through novel graph partitioning techniques. Thrust C is devoted to a software-defined-radio (SDR) based testbed for experimental data collection and testing, as well as evaluation of data cleaning methods for clutter and direct-path interference removal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.