Adaptive Interference Waveform Design Based on Reinforcement Learning
CHEN Tao1，2， ZHANG Ying1，2， HUANG Xiangsong1，2
1. College of Information and Communication Engineering， Harbin Engineering University， Harbin 150001，
Heilongjiang， China；2. Key Lab on Multi-Disciplines Cooperation Cognition Artificial Intelligence Technologies
and Applications of Heilongjiang Province， Harbin 150001， Heilongjiang， China
Abstract：Aiming at the problem that the traditional jamming algorithm cannot adapt to the change of signal environment, the Q-learning algorithm and the hypothesis method of "cutting" are combined to apply to the design of jamming waveform, so that the jamming waveform can achieve the effect of adaptive radar signal length change. The algorithm is mainly aimed at jamming the radar detection link. Constant false Alarm rate (CFAR) is adopted as the environment interaction model, and the sampling time and forwarding time of intermittent sampling signals are adjusted adaptively through enhanced learning. On this basis, the radar signals of unknown length are processed by "cutting" to achieve the purpose of optimal interference. Finally, simulation is carried out to realize the jamming of unknown radar signal. The simulation results show that the reinforcement learning algorithm can make full use of the historical data when making decision under the condition of uncertain signal model, and can achieve better interference effect compared with the traditional algorithm.
