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.
