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: 2332661
• 负责人: Ali Gurbuz
• 金额: $ 50万
• 依托单位: Mississippi State University
• 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=2332661&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频段，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检测、表征和减轻无源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中观察到的那样。 INTERACT（确保弹性主动/被动共存的集成测试平台）项目的目标是 1) 收集/整理具有地面实况信息的主动/被动射频共存数据集 2) 开发数据驱动的基于学习的射频干扰 (RFI) 检测以及由生成的数据启用的缓解方法。这些数据集将由部署在 NSF 的 AERPAW（高级无线空中实验和研究平台）平台上的机载无源微波辐射计系统收集。拟议的研究将通过无源传感方法、射频数据集和基于学习的射频干扰缓解方法进一步加深我们对频谱共享的理解。 INTERACT 项目提出了三项关键创新： 1）将开发基于无源辐射计系统的新型无人机系统（UAS）。该系统与涵盖不同几何形状、发射机参数和非限制频段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射频数据集，其中包含用于无源/有源射频共存的地面实况信息。 将开发 AERPAW 仿真环境中的无源辐射测量数字孪生，使实验人员能够在云环境中进行广泛而现实的射频缓解实验。 2) 将开发新型数据驱动的、基于学习的端到端 RFI 检测和缓解方法。所提出的解决方案将重点关注能够在时频域实现高分辨率 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，并且设计用于无源辐射计系统，以便检测和减轻 RFI，而无需在共存系统之间交换任何信息。 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智力优点和更广泛的影响审查进行评估，被认为值得支持标准。