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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.
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IEEE Spectrum Magazine, the flagship publication of the IEEE, explores the development, applications and implications of new technologies. It anticipates trends in engineering, science, and technology, and provides a forum for understanding, discussion and leadership in these areas.
Same tier 3 topics + same sponsor [3],
Same tier 3 topics + same partner [3],
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