Deceptive Jamming Mitigation Based on Proximal Policy Optimization

With the increasing reliance on radar technology in modern warfare, the operational capability of radar in countermeasure environments has become increasingly important. For waveform agile radars, randomly choosing transmit waveforms from a large set of orthogonal waveforms is not the optimal approach to mitigate the interference signal. Aiming at deception jamming mitigation, an anti-interference decision-making method based on proximal policy optimization (PPO) is proposed for waveform agile radars. By applying reinforcement learning algorithm, the radar can learn the strategy of the jammer through interaction with the environment and take the best action to obtain high return. This method effectively suppresses deception jamming while ensuring good detection performance of the radar. Numerical simulation results demonstrate the effectiveness of the proposed method.