Radar Jamming Decision-Making: A Review

The emergence of advanced radar systems, including multifunction radar (MFR) and cognitive radar, coupled with rapid advancements in anti-jamming technologies, has significantly reduced the effectiveness and response time of conventional radar jamming decision-making. To address these challenges, recent studies have integrated cognitive thinking into jamming decision-making, enabling the development of cognitive jamming systems. While prior research has explored jamming decision-making at various stages, a comprehensive, phase-based framework remains underdeveloped. This study bridges this gap by formalizing the mathematical logic of radar jamming and introducing a systematic decision-making framework encompassing four critical dimensions: 1) jamming resource allocation, 2) jamming pattern selection, 3) jamming waveform optimization, and 4) jamming timing determination. Leveraging the proposed framework, we conduct a detailed review and comparative analysis of the existing literature, categorizing approaches into traditional and cognitive jamming decision-making paradigms. We provide a focused review of reinforcement learning-based cognitive jamming decision-making with a systematic analysis of four critical aspects: problem modeling, radar state representation, reward function setting, and algorithmic implementation. Finally, we highlight emerging trends and provide forward-looking insights into the future evolution of radar jamming decision-making.