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

许多无线通信信号，如WiFi、蜂窝、无线电/电视和卫星，都可以作为副产品用于提供无源射频（RF）传感服务，包括对象和事件检测、定位、医疗保健监控等。无源射频感应能够定位非合作的无设备对象，这些对象通常比配备有源射频设备（例如，移动电话）的合作对象更难定位。然而，大多数先前的无源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，存在分辨率差、覆盖范围小、性能波动等问题。它们在利用较新的无线信号的较大带宽（BW）方面是无效的。旨在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，通过利用带宽丰富的无线信号（如5G/6G蜂窝、WiFi-6/7等），在复杂的室内和室外多径环境中定位无源rf静音移动物体。无源射频传感，它消除了对专用发射机的需要，是环保的。它是一种非侵入性的、经济的方法，不需要额外的基础设施投资。该项目的研究成果可以潜在地与现有的无线网络集成，使服务提供商能够为其客户提供无线通信和射频传感服务。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，为当地高中生提供暑期研究项目，并让PI与妇女、少数民族和代表性不足群体的学生合作。该项目的目标是开发一个基于连贯处理的框架，用于多路径环境中多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的重点是通过使用来自多个无线基站（BSs）的射频发射来开发无源定位和测绘技术，这些无线基站形成多静态传感几何结构，并提供监视区域的多角度。一项任务是开发一种直接映射方法，该方法直接从观测到的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。其他研究任务包括通过利用多静态传感系统中固有的空间和几何多样性来开发抗多路径定位方法，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。虽然推力A主要考虑集中式方法，可以访问所有BSs的测量，但推力B扩展了工作，并为所提出的多静态被动传感系统开发了分布式隐私保护定位和映射算法。目的是寻求鲁棒性、计算效率和隐私。后者的出现是因为多静态系统涉及到不同的BSs，这些BSs为不同的通信用户提供潜在的私有数据。所提出的算法在每个BS上并行运行，共享中间计算结果而不是原始数据以保护数据隐私，并且通过新颖的图分区技术产生最小的通信开销。推力C致力于一个基于软件定义无线电（SDR）的试验台，用于实验数据收集和测试，以及对杂波和直接路径干扰去除的数据清理方法进行评估。该奖项反映了美国国家科学基金会的法定使命，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.