Military Embedded Systems

DARPA launches Radio Frequency Machine Learning Systems program


August 15, 2017

Mariana Iriarte

Technology Editor

Military Embedded Systems

DARPA launches Radio Frequency Machine Learning Systems program
Image by DARPA

ARLINGTON, Va. Defense Advanced Research Projects Agency (DARPA) officials announced the new Radio Frequency Machine Learning Systems (RFMLS) program, which aims to further the cause of applying machine learning techniques to the realm of radio frequency (RF) signals.

“What I am imagining is the ability of an RF Machine Learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are ‘important’ from the background, and identifying those that don’t follow the rules,” says Paul Tilghman of DARPA’s Microsystems Technology Office.

He would want that same system to be able to discern subtle but inevitable differences in the RF signals from what otherwise are identical, mass-manufactured IoT devices and to distinguish these from signals intended to spoof or hack into these devices. “We want to be able to understand and trust what is happening in the Internet of Things and to stand up an RF forensics capability to identify unique and peculiar signals amongst the proverbial cocktail party of signals out there,” he adds.

The same situational awareness regarding the composition of RF signals in any given space should also support a wireless communications management paradigm known as spectrum sharing. That’s a paradigm of shared spectrum use rather than the current practice of exclusive allocations governed by license agreements for specific frequencies.

Tilghman is hoping to develop technologies to understand the current state of the spectrum for improved and extensive spectrum sharing both in the RFMLS program as well as in another major DARPA effort known as the Spectrum Collaboration Challenge.

The program wants to take artificial intelligence (AI) to the next level where RF applications of the emerging machine-learning wave of AI should yield far more agile and versatile capabilities: an RFML system, with a sufficiently rich training set of RF data, should be able to identify an enormous range of both known and previously unseen RF waveforms.

The RFMLS program features four technical components that would integrate into future RFML systems:

  • Feature Learning:  RFML systems will need to learn the characteristics used to identify and characterize signals in various civilian and military settings.
  • Attention and Saliency: An RFML system will need to include algorithms for directing its artificial attention to what is potentially important in the RF spectrum it is operating in. Researchers who win contracts to work on the RFMLS program will need to devise an equivalent within the RF domain of our own so-called salience detection, that is, the ability to identify and recognize important visual and auditory stimuli. The presence of a communications signal in a frequency band usually devoted to radar signals would be an example of a signal-of-interest that an RFMLS’s salience-detection capability would have to notice.
  • Autonomous RF Sensor Configuration: The RFML systems that DARPA envisions would have an equivalent ability to human eyes, where the system will automatically tune their receptivity to signals and signal features the systems deem to be most effective at accomplishing the task at hand.
  • Waveform Synthesis: A full RFML system also should be able to digitally synthesize virtually any possible waveform, much as human beings can pronounce any new word or add inflections or pauses to infuse gravitas or nuances of meaning into what they saying. This capability to create new waveforms tailored to the specific RF devices they emanate from should give other sophisticated radios the improved ability to identify friendly systems.

“If we get this right, we will have RF systems with the ability to discern and characterize signals in the ever-more-crowded spectrum. And that will give emerging automated systems, and the military commanders that rely on them, much needed information to understand the landscape of the wireless domain,” says Tilghman. “I hope our new RFMLS program will forge the technical foundations for a new domain and community of AI research.”

A Broad Agency Announcement describing the new RFMLS program, its goals, and how to submit proposals is posted on FedBizOpps.


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