The trend toward digital, programmable radio frequency (RF) equipment — epitomized by software-defined radio — means that radars can quickly change waveforms, creating unique signatures on the fly. In the increasingly congested and contested RF environment, hostile emitters become harder to locate, identify, jam and confuse. Hence today’s focus on Machine Learning applied to electronic warfare (EW) — or cognitive EW.
An important step along that path is improved spectrum awareness, one of the aims of the United States Defense Advanced Research Projects Agency’s (DARPA) RF Machine Learning Systems program. The program will lay the groundwork for “a new generation of RF systems that are goal-driven and can learn from data,” according to DARPA. It is one of multiple programs that address the RF/machine learning nexus. A contract for the program was recently awarded to BAE Systems, Expedition Technologies, Northeastern University, Teledyne Technologies and SRI.
When you can create “myriads of signals at any frequency in the RF spectrum,” it’s important to ask “what radio signals are actually occupying the set of frequencies in my immediate vicinity,” said the program’s manager, Paul Tilghman. The program is a “foundational” effort, Tilghman said. It’s building a technology base that would answer lots of questions, among which are how to improve EW and radar systems.
How to better understand the RF signal environment is the program’s “broad, high-level question,” he said. To get there, “to make sense of the spectrum data,” DARPA plans to develop fundamental algorithms and techniques that apply machine learning to the RF spectrum.
At a high level, DARPA is pursuing RF signal awareness as a means to expanding the capacity of the finite spectrum resource through improved spectrum sharing. “Systems trying to access the same block of spectrum at the same time, for example, might be able to negotiate over the time sequence,” said Chris Rappa, product line director for RF, EW and advanced electronics with BAE Systems’ FAST Labs research and development organization. Systems use spectrum to communicate, navigate, position, surveil and sense. “EW is only a subset of that spectrum negotiation piece,” said Rappa.
Spectrum awareness is also important as more radios, communications systems, radars, jammers and many other applications, including internet-of-things devices, operate in the spectrum and as hostile emitters become more clever at camouflaging their signatures to look like “white force” or neutral emitters. EW systems need to be able to infer the intent, friendly or not, of others sharing the spectrum.
DARPA has done some initial studies on “sample problem sets that are somewhat more simple in nature,” Tilghman said. In one effort, researchers built a convolutional neural network to understand what modulation a signal was using — AM, FM or phase-shift keying, for instance.
Those studies showed that the machine-learning system outperformed traditional approaches at every signal-to-noise ratio, Tilghman said. So even though that problem was relatively small in scope, it provided “enough evidence to go, ‘Oh, Wow!’” Tilghman added it proved that machine-learning systems “can abstract additional features and information out of the RF spectrum to help us better understand [the signal environment].”
As Google AI proved with the game of Go, “AI can tackle making decisions in really large combinatorial spaces,” he said. He hopes to use machine learning not only to process spectrum data once it’s collected, but also to “help us decide what spectrum data we will acquire with our RF sensors in the first place” by answering questions such as what spectrum to look at and capture, and when and where to look for it.
A cognitive system is capable of real-time learning. “It is thinking,” Rappa said.
