Improving the Capabilities of Cognitive Radar and Electronic Warfare Systems - Wiley Science and Engineering Content Hub
Download this complimentary White Paper today! This White Paper provides engineers and researchers with a comprehensive overview of how mode-agile threats are outpacing traditional static library radar/EW systems — and how cognitive AI/ML architectures enable adaptive, autonomous countermeasures in contested RF environments. What you will learn about: Why mode-agile threat emitters render traditional static threat library systems ineffective, deploying unexpected frequencies, modulation techniques, and hopping schemes that cannot be matched against predefined databases. How artificial neural networks, deep neural networks, fuzzy logic, and genetic algorithms form the AI/ML foundation of cognitive radar/EW systems capable of autonomous threat classification and real-time countermeasure generation. The key implementation challenges including computational resource demands at the tactical edge, minimizing detect-to-counter latency, wideband spectrum coverage, SWaP-C constraints, low probability of intercept modes, and assured position, navigation, and timing. How hardware-in-the-loop and system-in-the-loop training systems, combined with real-world signal collection and modelling/simulation software, enable iterative development and validation of cognitive AI/ML algorithms in controlled laboratory settings Click 'LOOK INSIDE' to Download Now.
Cognitive radar/EW systems use AI/ML to autonomously detect mode-agile RF threats, replacing static libraries. This shift to adaptive, real-time inference redefines critical infrastructure security: edge compute, latency budgets, and continuous learning models become core architectural decisions.
321 words~1 min read
Download this complimentary White Paper today!
This White Paper provides engineers and researchers with a comprehensive overview of how mode-agile threats are outpacing traditional static library radar/EW systems — and how cognitive AI/ML architectures enable adaptive, autonomous countermeasures in contested RF environments.
What you will learn about:
Why mode-agile threat emitters render traditional static threat library systems ineffective, deploying unexpected frequencies, modulation techniques, and hopping schemes that cannot be matched against predefined databases.
How artificial neural networks, deep neural networks, fuzzy logic, and genetic algorithms form the AI/ML foundation of cognitive radar/EW systems capable of autonomous threat classification and real-time countermeasure generation.
