Cognitive Electronic Warfare Application to Emitter Identification Process in Airborne Radar Warning Receiver Systems

Abstract

Cognitive electronic warfare (CEW) will soon replace conventional electronic warfare (EW) systems in the operational area. Although the CEW concept is utilized on various platforms, it has primarily emerged in military air platforms. Radar warning receiver (RWR) applications are essential in the CEW concept. Effective RWR systems accurately identify radar emitters in complex and dynamic environments, which is critical for situational awareness of air vehicle systems. systems provide information to countermeasure systems, increasing operational success by neutralizing enemy radar and defense systems. To sense the threats and apply appropriate countermeasures, such as jamming or dispensing chaff, requires a quick response and accurate RWR processes. RWR processes
are summarized as receiving, signal detection, deinterleaving, labeling, and emitter identification. This study investigates the application of machine learning (ML) techniques to the emitter identification process. The research demonstrates significant improvements in emitter identification accuracy and distance determination by using ML algorithms. The findings suggest that CEW strategies incorporating adaptive learning models can enhance RWR system performance in identifying and categorizing emitters, even in dense Radio frequency (RF) environments. In the trials, it is observed that while the decision tree, ensemble bagged, and support vector machines achieved 100% success in the classification step, the Gaussian method was the most successful, with a mean absolute percent error of 1.6 in the distance calculation step using regression. These results suggest a promising direction for developing more intelligent and resilient EW systems.

Cite this article as: İ. Çetin Taş and A. G. Pakfiliz, “Cognitive electronic warfare application to emitter identification process in airborne radar warning receiver systems,” Electrica, 2025, 25, 0182, doi: 10.5152/electrica.2025.24182.