Improving the Capabilities of Cognitive Radar & EW Systems
Tim Fountain & Leander Humbert
In this paper we will review the challenges that mode-agile or Wartime Reserve Mode (WARM) RADAR and
Electromagnetic Warfare (EW) threat emitters pose to traditional static threat library implementations in
RADAR and EW systems, review the architecture of cognitive Artificial Intelligence (AI) and Machine
Learning (ML) systems that can be used to deliver effective RF countermeasures. We will discuss how a
wideband RF record, simulation and playback system can be used to train AI/ML engines and evaluate the
response and effectiveness of those countermeasures on real hardware.
By downloading a white paper, the details of your profile might be shared with the creator of the content and you may be contacted by them directly.