SWIFT: LARGE: Adaptive Radio Frequency Interference Cancellation for Radio Science Observatories

科学射电观测站，包括雷达系统和射电望远镜，需要进入射电频谱进行实验。在某些情况下，可以在已经与通信系统重叠或受到无意射频干扰(RFI)的频率上进行实验。通过探索减少干扰的技术，我们将有助于维护科学测量的完整性，并提高无线电观测站在未来无线电频谱共享环境中运行的能力。开发的技术将侧重于识别和减轻无线电干扰的自动化方法。所采取的方法将包括先进的数学方法和人工智能技术。与现有方法相比，这些方法将非常有效，并可立即使用，以帮助提高射电观测站数据的质量。潜在的技术和算法很可能在本提案的直接背景下非常有用。它们将以Julia编程语言作为开放源码软件提供，以便科学界广泛使用。包括雷达系统和射电望远镜在内的科学射电观测站需要进入射电频谱进行实验。在某些情况下，试验仅限于可以保护的频段，但许多试验的频率已经与现有通信系统重叠，或者受到无意的射频干扰。我们的目标是在地球空间和天文学应用的实验无线电科学的背景下探索频谱共存的技术。我们的方法是探索麻省理工学院干草堆天文台系统自适应消除RFI的算法和数学技术的发展和应用。现代射电观测站必须与获得许可的和无意的RFI来源的大幅增加共存。这些观测站的实验通常需要测量显著低于背景热噪声下限的信号。这项工作的一个核心目标是开发能够使无线电科学系统免受干扰的算法和方法。从空时自适应滤波(STAP)和旁瓣抵消结构的现有方法出发，我们将实现一个稀疏参考传感器网络，并探索：(1)聚焦RFI的自动识别和分类方法，(2)加速算法的随机矩阵、草图和神经网络技术，以及(3)应用稀疏软件无线电网络为多个射电观测站传感器同时提供RFI抑制。我们提出的工作将在大规模无线电设施的背景下实现自适应消除RFI的高级算法。这些方法应普遍适用于更广泛的无线电设施和实验。在许多情况下，自适应抵消技术可以在频谱中实现否则可能不可用的观测。这些算法的有效应用及其使用高性能计算的缩放的数学方法将被演示。除了这项提议的直接背景之外，潜在的技术和具体的算法都可能非常有用。项目成果将在各个场所传播，涉及信号处理、计算机科学、数学和无线电科学(即URSI)等各个领域。审查后略微缩小了范围，将重点放在提案中与多通道旁瓣抵消结构有关的四个领域，而不是五个领域，具体减少的幅度将随着工作的进展而确定。虽然主要的项目目标保持不变，但作为范围缩小的一部分，该团队将利用较小的数据集来降低计算成本。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Scientific radio observatories, including radar systems and radio telescopes, require access to the radio spectrum for experiments. In some cases the experiments many be conducted in frequencies that already overlap communications systems or which are subject to unintentional radio frequency interference (RFI). By exploring techniques for mitigating the interference we will help to preserve the integrity of scientific measurements and improve the ability of radio observatories to operate in a future environment of radio spectrum sharing. The techniques developed will focus on automated methods to identify and mitigate radio interference. The approaches taken will include advanced mathematical methods and artificial intelligence techniques. These methods will be highly efficient compared to existing approaches and usable immediately to help improve the quality of radio observatory data. The underlying techniques and algorithms are likely to be highly useful beyond the immediate context of this proposal. They will be made available as open source software in the Julia programming language to enable widespread use by the scientific community. Scientific radio observatories including radar systems and radio telescopes require access to the radio spectrum for experiments. In some cases the experiments are limited to bands which can be protected but many are conducted in frequencies that already overlap existing communications systems or are subject to unintentional radio frequency interference (RFI). We aim to explore techniques for spectral coexistence in the context of experimental radio science for Geospace and Astronomy applications. Our approach is to explore the development and application of algorithms and mathematical techniques for adaptive cancellation of RFI for systems at MIT Haystack Observatory. Modern radio observatories must co-exist with a vast increase in both licensed and unintentional sources of RFI. The experiments at these observatories often require the measurement of signals that are significantly below the background thermal noise floor. A core goal of the effort is to develop algorithms and methodologies that will enable interference-immune radio science systems. Starting from existing approaches to space time adaptive filtering (STAP) and a sidelobe cancelling architecture we will implement a sparse reference sensor network and explore: (1) automated methods for identification and classification of RFI focused, (2) random matrix, sketch, and neural network techniques to accelerate the algorithms, and (3) the application of sparse software radio networks to provide simultaneous RFI mitigation for multiple radio observatory sensors. Our proposed work will enable advanced algorithms for adaptive cancellation of RFI in the context of a large scale radio facility. The approaches should be generally applicable to a wider range of radio facilities and experiments. In many cases the adaptive cancellation techniques may enable observations in spectrum that might otherwise not be usable. Mathematical approaches to the efficient application of these algorithms and their scaling using high performance computing will be demonstrated. Both the underlying techniques and the specific algorithms are likely to be highly useful beyond the immediate context of this proposal. Project results will be disseminated in venues, spanning communities from signal processing, computer science, mathematics, and Radio Science (i.e. URSI). The scope was slightly reduced after review to focus on four areas of the proposal associated with the multi-channel sidelobe cancellation architecture instead of five, with the specific reduction being determined as the work progresses. While the major project goals remain unchanged, as part of the scope reduction, the team will utilize smaller datasets to reduce computational costs.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.