AI and Machine Learning Enable Tactical Cognitive EW for the Soldier

With a hand-held device, a soldier can see where enemy signals are coming from, stretched out on a low-resolution virtual plane.

With a hand-held device, a soldier can see where enemy signals are coming from, stretched out on a low-resolution virtual plane.

October 24, 2016 | Source: Kelsey D. Atherton, Popular Science

With this hand-held cogintive EW device, a soldier can see where enemy signals are coming from presented on a virtual plane.

This morning, BAE revealed a “lightweight, handheld tactical sensor” for cognitive electronic warfare. Developed for DARPA, the sensor is designed for soldiers and marines to carry into battle, where it will identify and classify new signals. In the previous, Cold War-era approach, American troops went into battle knowing only signals already identified, and after detecting a new signal, it would take months to have a response. Many systems, even ones that already had signals recorded in peacetime, can, according to Niedzwiecki, "have a “war reserve” mode, a waveform signature that we may not have seen before,” so there’s now guarantee that adapting to an old signal will protect against the signals that replace it.

Cognitive electronic warfare takes the burden of deciphering and countering new signals out of human minds in a lab, and entrusts it instead to AI and machine learning. If the cognitive system sees a signal similar to one it has seen before, it can respond accordingly. If the interference makes the radio signal weak, then the cognitive system might just put more power into the same radio signal. If the jamming makes some parts of the spectrum unworkable, then the cognitive system could look for a part of the spectrum that’s still open for communication, and redirect signals there. If the approach works, as captured by bit-error rate, the system learns and remembers. If it doesn’t, the cognitive system can try a different approach, changing up its response until something gets through.