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Artificial Intelligence and Machine learning in Electronic Warfare


 Three closely related Defense Advanced Research Projects Agency (DARPA) programs apply artificial intelligence to the electromagnetic spectrum and will likely result in electronic warfare (EW) systems with unprecedented autonomy. The first two—Adaptive Radar Countermeasures (ARC) and Behavioral Learning for Adaptive Electronic Warfare (BLADE) are considered sister programs. Both apply artificial intelligence, or AI, to EW systems.

ARC, for example, aims to enable airborne EW systems to automatically generate effective countermeasures against new, unknown and adaptive radars in real time. ARC technology is designed to isolate unknown radar signals in the presence of other hostile, friendly and neutral signals; deduce the threat posed by that radar; synthesize and transmit countermeasure signals; and assess the effectiveness of countermeasures based on over-the-air, observable threat behaviors.

ARC technologies use an open architecture to allow for insertion, modification and removal of software modules with minimal effect on other elements of the system. ARC algorithms and signal-processing software will be suitable both for new EW systems and for retrofitting into existing systems without extensively reworking front-end radio frequency hardware, according to a DARPA webpage.

“ARC, as its name implies, is a cognitive EW program specifically targeting radar systems. It’s jamming carried out against radars,” Tilghman says.

The BLADE program is developing machine learning algorithms and techniques to rapidly detect and characterize new radio threats, dynamically synthesize new countermeasures, and similar to ARC, provide accurate battle damage assessment based on over-the-air, observable changes in the threat. The technology is designed to counter new and dynamic wireless communication threats in tactical environments. It also could enable a shift from a manual-intensive, lab-based countermeasure development approach to an adaptive, in-the-field systems approach.

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