A Probabilistic Jamming Strategy Model for Frequency Agility Radar Anti-Jamming Problem

With the development of electronic warfare, the jammer is much smarter than before and can adopt different strategies to jam the radar. To design efficient anti-jamming strategies, the radar anti-jamming problem is recently modeled as a Markov decision process (MDP) problem and reinforcement learning (RL) algorithms are used to solve it. However, common RL algorithms needs a large number of interaction samples, which is not realistic in practice. To address this problem, we propose a probabilistic jamming strategy model for the frequency agility radar (FA) that can learn the unknown jamming strategy through fewer interaction samples. Based on proposed model, a jamming strategy is first expressed by a series of unknown probability matrixes and weights. Then, the FA radar is used to induce jammer to emit jamming signals and collects samples of radar-jammer interaction in MDP. The model parameters are estimated by means of maximum likelihood estimation (MLE). Finally, RL algorithm can be used to train the anti-jamming strategies through offline interaction. Simulation results illustrate the effectiveness of the proposed model.