Why AI-Driven Cognitive Systems Are Redefining Radar and Electronic Warfare
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 Learn Why 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…
Mode-agile threats pose significant challenges to conventional radar and electronic warfare (EW) systems. These threats involve unexpected frequencies, modulation techniques, and hopping schemes that are not present in static library databases. As a result, legacy electronic protect, attack, and support systems struggle to respond effectively.
The introduction of AI/ML cognitive architectures is reshaping the landscape of radar and EW. By employing artificial neural networks (ANN), deep neural networks (DNN), fuzzy logic, and genetic algorithms, these systems can autonomously classify threats, de-interleave signals, and generate real-time countermeasures without human intervention.
A cognitive radar/ EW system comprises several functional blocks. RF acquisition and search and tracking are crucial for detecting and monitoring targets. The core AI/ML signal analysis block interprets the received data, while waveform synthesis generates adaptive signals. RF generation then transmits these signals back into the environment. This closed-loop system enables the cognitive system to perceive, learn, reason, and act autonomously.
Training and validating AI/ML algorithms in cognitive systems requires sophisticated tools. Wideband RF record, simulation, and playback testbeds, coupled with modeling and simulation software, allow for iterative algorithm refinement, regression testing, and mission preparation. These controlled laboratory environments ensure the cognitive system is well-prepared for real-world scenarios.
Written by urgent.news from IEEE Spectrum's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
