Collaborative Research: SWIFT-SAT: INtegrated Testbed Ensuring Resilient Active/Passive CoexisTence (INTERACT): End-to-End Learning-Based Interference Mitigation for Radiometers

合作研究：SWIFT-SAT：确保弹性主动/被动共存的集成测试台 (INTERACT)：基于端到端学习的辐射计干扰缓解

• 批准号: 2332662
• 负责人: Mehmet Kurum
• 金额: $ 25万
• 依托单位: University of Georgia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332662&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; SAT; INtegrated

As next-generation communication and satellite systems utilize more frequency bands, the potential interference risks to passive radiometer sensors used for environmental and atmospheric sensing are increasing. Thus, it is imperative to develop efficient methods to detect, characterize and mitigate anthropogenic sources of interference at passive radiometers. Radio frequency (RF) research domains, specifically those addressing the active/passive coexistence, are in critical need of datasets that enable learning-based detection, identification, and classification, as was observed in image processing domains. The goals of the project INTERACT (INtegrated Testbed Ensuring Resilient Active/Passive CoexisTence) are 1) to collect/to currate active/passive RF coexistence datasets with ground truth information 2) to develop data-driven learning-based RF interference (RFI) detection and mitigation approaches enabled by the generated data. The datasets will be collected by an airborne passive microwave radiometer system to be deployed on the NSF's AERPAW (Aerial Experimentation and Research Platform for Advanced Wireless) platform. The proposed research will further our undertanding on spectrum sharing through passive sensing methods, RF datasets, and learning based RFI mitigation approaches. The project INTERACT proposes three key innovations: 1) A new Unmanned Aerial System (UAS) based passive radiometer system will be developed. This system together with the experimental development of various active transmission scenarios covering different geometries, transmitter parameters and waveforms at non-restricted bands will result in the first-ever large experimental RF dataset with ground truth information for passive/active RF coexistence. A digital twin for passive radiometry in the emulation environment of AERPAW will be developed to enable experimenters to facilitate extensive, yet realistic RF mitigation experiments in a Cloud environment. 2) Novel data-driven end-to-end learning-based RFI detection and mitigation approaches will be developed. The proposed solutions will focus on approaches that can achieve high-resolution RFI detection in the time-frequency domains, learning based radiometer calibration, and joint mitigation to estimate the scientific observation of radiometers under RFI. These solutions do not require centralized servers and are designed to work on passive radiometer systems in order to detect and mitigate RFI without any information exchange between coexisting systems. 3) The research will produce new deep reinforcement learning and subspace-based RFI mitigation approaches using the feedback from active and passive systems.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.

随着下一代通信和卫星系统使用更多的频带，用于环境和大气传感的无源辐射计传感器的潜在干扰风险正在增加。因此，必须开发有效的方法来探测、表征和减轻被动辐射计的人为干扰源。射频（RF）研究领域，特别是那些解决主动/被动共存的领域，迫切需要能够实现基于学习的检测，识别和分类的数据集，正如在图像处理领域所观察到的那样。INTERACT（确保弹性有源/无源兼容性的集成测试平台）项目的目标是：1）收集/使用地面实况信息处理有源/无源RF共存数据集2）开发数据驱动的基于学习的RF干扰（RFI）检测和缓解方法。这些数据集将由一个机载被动微波辐射计系统收集，该系统将部署在NSF的AERPAW（高级无线空中实验和研究平台）平台上。拟议的研究将通过被动感知方法，RF数据集和基于学习的RFI缓解方法进一步加深我们对频谱共享的理解。INTERACT项目提出了三个关键创新：1）将开发一种新的基于无人机系统（UAS）的被动辐射计系统。该系统与涵盖不同几何形状、发射机参数和非限制频带波形的各种有源传输场景的实验开发一起，将产生有史以来第一个大型实验RF数据集，其中包含无源/有源RF共存的地面实况信息。 将开发AERPAW仿真环境中被动辐射测量的数字孪生模型，以使实验人员能够在云环境中进行广泛而现实的RF缓解实验。2)将开发新的数据驱动的端到端学习RFI检测和缓解方法。拟议的解决方案将侧重于可以实现时频域中的高分辨率RFI检测、基于学习的辐射计校准以及联合缓解以估计RFI下辐射计的科学观测的方法。 这些解决方案不需要集中式服务器，并且设计用于被动辐射计系统，以便在共存系统之间没有任何信息交换的情况下检测和减轻RFI。 3)该研究将利用主动和被动系统的反馈产生新的深度强化学习和基于子空间的RFI缓解方法。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。