Collaborative Research: CNS Core: Large: 4D100: Foundations and Methods for City-scale 4D RF Imaging at 100+ GHz

合作研究：CNS 核心：大型：4D100：100 GHz 城市规模 4D 射频成像的基础和方法

• 批准号: 2215646
• 负责人: Upamanyu Madhow
• 金额: $ 96万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215646&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Large

Advances in low-cost low-power silicon radio frequency (RF) integrated circuits (ICs) in the last two decades have opened up the commercial applications for millimeter wave (mmWave) frequencies which are an order of magnitude beyond those used in WiFi and cellular today. Large-scale deployment of mmWave communication networks, such as NextG cellular infrastructure outdoors and NextG WiFi infrastructure indoors, implies that these resources can be leveraged for RF imaging at scales that are not otherwise possible. The project develops foundational algorithms, architectures and protocols for such Joint Communication and Imaging (JCAI) systems. Each sensor in such a system provides 4D measurements (range, Doppler, azimuth angle and elevation angle) whose resolution improves by going to higher frequencies. The project establishes US leadership in a critical technology by developing large-scale RF imaging using frequencies beyond 100 GHz. Outdoor applications include pedestrian and vehicular tracking for global situational awareness supporting vehicular autonomy, and addressing security challenges such as timely detection of illegal drones or unauthorized personnel. In indoor settings, the technology enables fine-grained inference/prediction of human actions for eldercare and smart home applications. RF imaging technologies are especially useful in low-light or high-smoke/fog conditions when visible light or infrared technologies are not effective.The project develops and demonstrates a framework for JCAI at mmWave frequencies. A core aspect of the technical plan is to drastically improve resolution by synthesizing large apertures (Thrust 1). This employs a combination of novel approaches to single sensor design which utilize large antenna arrays developed for communication, and networked collaboration between multiple sensors. A complementary aspect (Thrust 2) is the strategic utilization of unmanned vehicles to image difficult-to-reach areas, utilizing the fixed infrastructure to reduce the robot payload. In Thrust 3, hardware at 140 GHz previously developed by the PIs for communication will be adapted to support demonstration of networked RF imaging at 100+ GHz. Thrust 4 develops a control plane for networked imaging, including a resource management framework based on imaging demand and imaging capacity, and protocols supporting collaborative imaging. The concepts and methods to be developed have potential impact in a vast array of applications, including vehicular autonomy and road safety, manufacturing automation, indoor and outdoor security, eldercare, and healthcare. The PIs will work closely with industry partners, building on their strong track record in transitioning mmWave research, and plan to incorporate this research into the undergraduate curriculum through courses, capstone projects, and REU projects.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.

过去二十年来，低成本低功耗硅射频 (RF) 集成电路 (IC) 的进步开启了毫米波 (mmWave) 频率的商业应用，其频率比 WiFi 和蜂窝网络中使用的频率高出一个数量级今天。毫米波通信网络的大规模部署，例如室外的 NextG 蜂窝基础设施和室内的 NextG WiFi 基础设施，意味着这些资源可以用于大规模的射频成像，而这是其他方式不可能实现的。该项目为此类联合通信和成像（JCAI）系统开发基础算法、架构和协议。此类系统中的每个传感器都提供 4D 测量（距离、多普勒、方位角和仰角），其分辨率通过提高频率而提高。 该项目通过使用超过 100 GHz 的频率开发大规模射频成像，确立了美国在关键技术方面的领先地位。户外应用包括行人和车辆跟踪，以实现全球态势感知，支持车辆自主，并解决及时检测非法无人机或未经授权的人员等安全挑战。 在室内环境中，该技术可以对老年人护理和智能家居应用的人类行为进行细粒度的推断/预测。当可见光或红外技术无效时，射频成像技术在低光或高烟/雾条件下特别有用。该项目开发并演示了毫米波频率下的 JCAI 框架。 该技术计划的一个核心方面是通过合成大孔径来大幅提高分辨率（Thrust 1）。它采用了单一传感器设计的新颖方法组合，利用为通信而开发的大型天线阵列以及多个传感器之间的网络协作。一个补充方面（主旨 2）是战略性地利用无人驾驶车辆对难以到达的区域进行成像，利用固定基础设施来减少机器人的有效负载。在 Thrust 3 中，PI 先前开发的用于通信的 140 GHz 硬件将进行调整，以支持 100+ GHz 网络射频成像的演示。 Thrust 4开发了网络成像的控制平面，包括基于成像需求和成像能力的资源管理框架，以及支持协作成像的协议。待开发的概念和方法对广泛的应用具有潜在影响，包括车辆自主和道路安全、制造自动化、室内和室外安全、老年护理和医疗保健。 PI 将与行业合作伙伴密切合作，以他们在毫米波研究转型方面的良好记录为基础，并计划通过课程、顶点项目和 REU 项目将这项研究纳入本科课程。该奖项反映了 NSF 的法定使命，并被视为值得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来支持。