Why AI-Driven Cognitive Systems Are Redefining Radar and Electronic Warfare
IEEE Spectrum AI Rohde & Schwarz
AI-driven cognitive radar and electronic warfare systems use machine learning to automatically detect and counter mode-agile threats that evade traditional static library-based defense systems. These systems employ neural networks and genetic algorithms to classify signals, de-interleave emissions, and generate countermeasures in real-time without human intervention. Adaptive AI/ML architectures enable military electronic protection and attack systems to respond to unpredictable enemy frequencies and modulation techniques that legacy systems cannot handle.
Why it matters
An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures.What Attendees will LearnWhy mode-agile threats render static library systems ineffective — Explore how wartime reserve modes and mode-agile emitters deploy unexpected frequencies, modulation techniques, and hopping schemes that cannot be matched against traditional threat databases, leaving legacy electronic protect, attack, and support systems unable to respond.How AI/ML techniques power cognitive radar/EW systems — Understand the roles of artificial neural networks (ANN), deep neural networks (DNN), fuzzy logic, and genetic algorithms in enabling autonomous threat classification, signal de-interleaving, and real-time countermeasure generation without human intervention.The architecture of a cognitive radar/EW system — Examine the functional blocks including RF acquisition, search and tracking, core AI/ML signal analysis, wa
