Effective jamming techniques for communication and control signals are required in modern electronic warfare to counter unmanned aerial vehicles (UAVs) with three-dimensional mobility. In this paper, we propose a hierarchical deep reinforcement learning (HDRL)-based cooperative jamming method using ground jammers (GJs) and UAV jammers (UJs) for battlefield network security. The proposed method aims to maximize the jamming effect on malicious UAVs (MUs) through two types of jammers. In particular, the proposed method reduces the computational complexity of jamming through a hierarchical framework.
This paper is based on research supported by LIG Nex1, funded y the government (Defense Acquisition Program Agency) in 2022. (2022U145009, 50%) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2022-0-00704, Development of 3D-NET Core Technology for High-Mobility Vehicular Service, 50%).
