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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
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IEEE Spectrum and Wiley are proud to bring you this White Paper, sponsored by Rohde & Schwarz
More Information
With today’s emerging threats, traditional radar and electronic warfare (EW) systems that rely on static threat libraries face a critical vulnerability: mode-agile emitters operating in non-traditional modes that cannot be matched against predefined databases. A cognitive RF system addresses this challenge through artificial intelligence and machine learning techniques, enabling autonomous perception, reasoning, and response to unknown threats in the RF spectrum. This white paper reviews the architecture of cognitive AI/ML radar and EW systems, including key functional blocks such as RF acquisition, AI-driven analysis and inferencing, waveform synthesis, and RF generation. It also examines the challenges of training these systems — from acquiring real-world and simulated signal datasets to performing hardware-in-the-loop (HIL) and system-in-the-loop (SIL) testing — and describes how closed-loop testbeds can iteratively develop, validate, and improve the AI/ML algorithms needed to counter unknown threats.
