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

利用带宽丰富的无线信号对射频静音移动物体进行无源定位

• 批准号: 2212940
• 负责人: Hongbin Li
• 金额: $ 36万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212940&HistoricalAwards=false
• 关键词: Leveraging; Bandwidth; Rich; Wireless; Signals

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.

许多无线通信信号、WiFi、蜂窝、无线电/电视和卫星都可以用来提供无源射频 (RF) 传感服务作为副产品，包括对象和事件检测、定位、医疗保健监控等。无源 RF 传感能够定位非合作无设备对象，这些对象通常比配备有源 RF 设备（例如移动设备）的合作对象更难定位。 电话）。然而，大多数现有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而存在分辨率差、覆盖范围小和性能波动的问题。它们无法有效利用较新无线信号的更大带宽 (BW)。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/6G 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路径环境中定位无源射频静音移动物体。无源射频传感无需专用发射器，是绿色环保的。它是非侵入性且经济的，不需要额外的基础设施投资。该项目的研究成果有可能与现有无线网络集成，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，针对当地高中生的暑期研究项目，以及 PI 与女性、少数族裔和代表性不足群体的学生合作的机会。该项目的目标是开发一个基于连贯处理的框架，用于在多路径环境中对多个目标进行稳健的高分辨率定位和映射。它包括三个研究重点。 Thrust A 专注于通过利用多个无线基站 (BS) 的射频发射来开发无源定位和测绘技术，形成多静态传感几何结构并提供监视区域的多视角。其中一项任务是开发一种直接绘图方法，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固有的空间和几何多样性来开发抗多径定位方法，以及多速率快速/慢速采样方法，该方法可以实现解耦位置和速度估计以降低复杂性。 Thrust A 主要考虑可以访问所有 BS 测量的集中式方法，而 Thrust B 则扩展了工作范围，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。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不同的基站，为不同的通信用户提供潜在的私有数据服务。所提出的算法在每个基站上并行运行，共享中间计算结果而不是原始数据以保护数据隐私，并通过新颖的图分区技术将通信开销降至最低。 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，以及评估杂波和直接路径干扰消除的数据清理方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Outlier-Detection-Based Robust Information Fusion for Networked Systems

• DOI: 10.1109/jsen.2022.3212908
• 发表时间: 2019-09
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Hongwei Wang;Hongbin Li;Wei Zhang;J. Zuo;Heping Wang;Jun Fang

Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar

• DOI: 10.1016/j.sigpro.2022.108785
• 发表时间: 2022-09
• 期刊: Signal Process.
• 作者: Junhu Ma;Hongbin Li;Lu Gan

Confederated Learning: Federated Learning With Decentralized Edge Servers

• DOI: 10.1109/tsp.2023.3241768
• 发表时间: 2022-05
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Bin Wang;Jun Fang;Hongbin Li;Xiaojun Yuan;Qing Ling

Joint Beamforming and Channel Reconfiguration for RIS-Assisted Millimeter Wave Massive MIMO-OFDM Systems

• DOI: 10.1109/tvt.2023.3243389
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Hanyu Wang;Jun Fang;Hongbin Li

Clutter Suppression for Target Detection Using Hybrid Reconfigurable Intelligent Surfaces

• DOI: 10.1109/radarconf2351548.2023.10149583
• 发表时间: 2023-05
• 期刊: 2023 IEEE Radar Conference (RadarConf23)
• 作者: Fangzhou Wang;Hongbin Li;A. L. Swindlehurst