How AI-Driven Cognitive Systems Are Redefining Radar and Electronic Warfare
This white paper from IEEE Spectrum and Wiley explains how traditional radar and electronic warfare systems are vulnerable to mode-agile threats, and how cognitive AI/ML architectures enable adaptive countermeasures. It covers the key components of cognitive RF systems, training challenges, and closed-loop testbeds for algorithm development.
Traditional radar and electronic warfare systems rely on static threat libraries, but mode-agile emitters can operate in non-traditional modes that evade predefined databases. A cognitive RF system uses artificial intelligence and machine learning for autonomous perception, reasoning, and response to unknown threats in the RF spectrum. The white paper reviews the architecture of such systems, including RF acquisition, AI-driven analysis and inferencing, waveform synthesis, and RF generation. It also discusses training challenges such as acquiring real-world and simulated signal datasets, hardware-in-the-loop and system-in-the-loop testing, and describes how closed-loop testbeds iteratively develop, validate, and improve AI/ML algorithms to counter unknown threats.
- Сокращения
- EW = Electronic Warfare — радиоэлектронная борьба
- ML = Machine Learning — машинное обучение
- RF = Radio Frequency — радиочастота
- HIL = Hardware-in-the-loop — аппаратно-в-контуре
- SIL = System-in-the-loop — система-в-контуре
- SWaP-C = Size, Weight, Power, and Cost — размер, вес, мощность и стоимость
Source: IEEE Spectrum AI —
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