The goal of the Radio Frequency Machine Learning Systems (RFMLS) Program is to develop the foundations for applying modern data-driven Machine Learning (ML) to the RF Spectrum domain. These innovations form the basis of a new wave of Signal Processing technologies to address performance limitations of conventionally designed radio frequency (RF) systems such as radar, signals intelligence, electronic warfare, and communications.
Over the last decade ML has been applied successfully to numerous sensor modalities, and is now common place in many commercial applications, including object and facial recognition in images, speech recognition in acoustic signals, and text parsing and reasoning from documents. Key to each of these innovations was the evolution from hand-engineered approaches tailored to each problem, to solutions that learned from large datasets.
RF systems conversely are still designed using models and equations based on idealized assumptions and approximations regarding hardware, environment, and the problem being solved. The inaccuracy of these assumptions challenge our ability to perform tasks such as identification of signals among the ever-increasing myriad which populate the wireless landscape.
Under the program, RFMLS systems will seek to learn to perform four specific tasks. Each task emphasizes a core constituent capability of RF ML. The four solutions can be combined and applied to address DoD operational needs in the RF Spectrum.
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