The difference between today's tech and that of the 1970s lies in the adoption of readily available digital processing. Such processing effectively allows operators to change aspects of the waveforms that radar and communications systems use. "The problem now is that if we continue to rely on that [old] approach, the radar waveforms we're expecting could be rapidly changed," Tilghman says. "Cognitive EW is being developed to deal with the unexpected."
DARPA's new approach uses machine learning algorithms to assess and characterize radar and communications emitters in real time. It learns their characteristics in the moment and then produces a countermeasure. It's not that the system is inventing new countermeasures on the fly. Rather, Tilghman says, cognitive EW "deduces the right set of countermeasures to employ." Of course, the secretive agency won't say how quickly the AI can assess and respond, only that the "time frame is sufficient to meet the needs of countering that radar."
This cognitive EW effort began in 2010 and is broken into two parts: Adaptive Radar Countermeasures (ARC) and Behavioral Learning for Adaptive Electronic Warfare (BLADE). The two tracks exist because of the differing nature of thwarting an enemy's radar and its communications.
When cognitive EW makes its debut, possibly within a decade, observers speculate that it will function alongside modern systems like the Navy's Next Generation Jammer (used on the EA-18G Growler EW aircraft) or as an adjunct to the jamming capability of the F-35's active electronically scanned array radar (AESA). DARPA won't specify. "We're focused on the algorithms, the AI," Tilghman says. "The actual EW system we use is meant to potentially be anything."
The application of artificial intelligence to a variety of tangential areas is almost certain to follow. DARPA kicked off its Spectrum Collaboration Challenge earlier this year, challenging competitors to develop collaborative autonomous spectrum systems that work together to optimize bandwidth in dense communications environments.
SOURCES - DARPA, Popular Mechanics