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）的进步开辟了毫米波（mmWave）频率的商业应用，这些频率比当今WiFi和蜂窝中使用的频率高出一个数量级。毫米波通信网络的大规模部署，例如NextG室外蜂窝基础设施和NextG室内WiFi基础设施，意味着这些资源可以用于以其他方式无法实现的规模进行RF成像。该项目为这种联合通信和成像（JCAI）系统开发基础算法，架构和协议。这种系统中的每个传感器提供4D测量（距离、多普勒、方位角和仰角），其分辨率通过转到更高的频率而提高。 该项目通过开发使用100 GHz以上频率的大规模RF成像，确立了美国在关键技术方面的领导地位。户外应用包括行人和车辆跟踪，用于支持车辆自主的全球态势感知，并解决安全挑战，如及时检测非法无人机或未经授权的人员。 在室内环境中，该技术可以对老年人护理和智能家居应用中的人类行为进行细粒度的推断/预测。射频成像技术在可见光或红外技术无法发挥作用的低光或高烟/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（推力1）。这采用了一种新的方法，利用大型天线阵列开发的通信和多个传感器之间的网络协作的单一传感器设计的组合。一个补充方面（推力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，利用固定的基础设施减少机器人的有效载荷。在Thrust 3中，PI之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RF成像的演示。 Thrust 4为网络成像开发了一个控制平面，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在广泛的应用中具有潜在的影响，包括车辆自动化和道路安全，制造自动化，室内和室外安全，老年人护理和医疗保健。 PI将与行业合作伙伴密切合作，建立在他们在过渡毫米波研究方面的良好记录，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，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。