For decades, military radar and electronic warfare systems worked like a phrasebook. Engineers built a library of known enemy signals, and when a system detected one, it looked up the matching entry and responded with a pre-planned countermeasure.
That approach is breaking down. According to IEEE Spectrum, so-called mode-agile threats — radars and jammers that can change their behavior on the fly — render static library systems ineffective. If the signal isn't in the book, the book is useless.
IEEE Spectrum points to one reason the problem is hard to solve by simply expanding the library: wartime reserve modes. These are signal behaviors an adversary deliberately withholds during peacetime, keeping them out of the intelligence catalogs that libraries are built from, and revealing them only in actual conflict. A system that has never seen a mode cannot look it up.
The proposed answer, per the same source, is a shift toward AI and machine-learning cognitive architectures. Rather than matching against a fixed list, these systems are designed to characterize an unfamiliar signal as they encounter it and adapt their countermeasures in real time — learning in the field instead of relying entirely on what was loaded before deployment.
The material comes from an industry educational session hosted on Wiley's KnowledgeHub, framed around what attendees will learn, so it reflects where the defense-electronics field believes it is heading rather than a report on fielded, proven systems.
Still, the direction matters beyond defense contractors. It reflects a broader pattern in which AI is being pushed into split-second decisions that once depended on human-curated reference data — and in electronic warfare, those decisions happen faster than any operator can review them.