White PaperDefense
Improving The Capabilities of Cognitive Radar and EW Systems
SPONSORED BY: ![]()
This white paper examines how mode‑agile (WARM) radar and EW threat emitters undermine traditional static threat‑library approaches and proposes a cognitive AI/ML architecture for robust RF countermeasures. We detail a wideband RF record‑simulation‑playback platform that trains the AI/ML engines and validates their response effectiveness on actual hardware, demonstrating a dynamic, adaptive defense against evolving RF threats.
Don't have an account?
Overview
This white paper by Rohde & Schwarz addresses the challenges posed by mode-agile and wartime reserve mode (WARM) radar and electronic warfare (EW) threat emitters to traditional radar and EW systems that rely on static threat libraries. Traditional systems classify threats based on known parameters stored in fixed databases, but mode-agile emitters operate in unpredictable ways, rendering static libraries ineffective. To counter this, the paper advocates for the use of cognitive radar and EW systems that leverage artificial intelligence (AI) and machine learning (ML) techniques to dynamically perceive, learn, reason, and respond to RF threats in real time.
The paper outlines core components of cognitive radar/EW systems, including RF acquisition hardware, search and tracking subsystems, AI/ML-driven signal analysis and threat counter-solution engines, and RF generation modules for jamming or deception. Key AI/ML techniques such as artificial neural networks, deep neural networks, fuzzy logic, and genetic algorithms are summarized, highlighting their complementary strengths and challenges like computational complexity and latency.
A significant focus is placed on the data requirements for training AI/ML algorithms. Real-world signal collection is difficult, especially for rare or new threat modes, so the paper proposes combining live data capture using commercial off-the-shelf (COTS) systems like Rohde & Schwarz’s IRAPS—with advanced signal visualization (ZoomOut®) and vector signal generation tools—to build diverse, representative datasets. These datasets support supervised learning and iterative algorithm refinement.
The document also details a hardware-in-the-loop (HIL) and system-in-the-loop (SIL) training environment using the ERISYS SigPro-4000 platform integrated with RF signal generators and analyzers. This setup facilitates closed-loop training and evaluation of AI/ML algorithms under realistic conditions, enabling performance tuning and regression testing before deployment.
Finally, the paper discusses key system design challenges such as minimizing detect-to-counter latency, managing wide bandwidth and dynamic range requirements, and meeting size, weight, and power constraints while ensuring secure communications and timing synchronization amid contested environments.
In conclusion, the white paper advocates cognitive AI/ML radar and EW systems supported by advanced data collection, simulation, and training platforms as vital to outpacing evolving RF threats and safeguarding critical assets in modern electronic warfare.