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利用更多的频带，用于环境和大气传感的被动辐射仪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检测，表征和减轻被动辐射仪干扰的人为来源。射频频率（RF）研究域，特别是针对主动/被动共存的研究域，非常需要数据集，这些数据集可实现基于学习的检测，识别和分类，如图像处理域中所观察到的那样。项目相互作用的目标（集成测试台，确保有弹性的主动/被动共存）是1）收集/与地面真相信息的活动/被动RF共存数据集2）开发基于数据驱动的基于数据的RF干扰（RFI）检测和缓解方法，并由生成的数据启用。数据集将由空降的无源微波辐射计收集，该系统将部署在NSF的Aerpaw（Adverial实验和高级无线研究平台）平台上。拟议的研究将通过被动传感方法，RF数据集和基于学习的RFI缓解方法来进一步我们对频谱共享的承诺。该项目互动提出了三个关键创新：1）将开发出新的基于无人机的无源辐射计系统。该系统以及各种主动传输方案的实验开发涵盖了不同的几何形状，在非限制频段处的发射器参数和波形将导致有史以来的第一个大型实验RF数据集，并具有地面真相信息，以实现被动/主动RF共存的地面真相信息。 将开发用于Aerpaw仿真环境中的被动辐射指定的数字双胞胎，以使实验者能够在云环境中促进广泛但现实的RF缓解实验。 2）将开发基于数据驱动的端到端学习的新型RFI检测和缓解方法。所提出的解决方案将集中在可以在时频域中实现高分辨率RFI检测的方法，基于学习的辐射仪校准以及关节缓解措施，以估计RFI下辐射仪的科学观察。 这些解决方案不需要集中式服务器，旨在在被动辐射计系统上工作，以检测和减轻RFI，而无需共存系统之间的任何信息交换。 3）该研究将使用主动和被动系统的反馈来生产新的深入强化学习和基于子空间的RFI缓解方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来评估的评估值得支持的。