Sensing Jamming Strategy From Limited Observations: An Imitation Learning Perspective

This paper studies the problem of sensing mainlobe jamming strategy through interaction samples between a frequency agile radar and a transmit/receive time-sharing jammer. We model this interaction as an episodic Markov decision process, where the jammer's strategy is treated as the state transition probability that needs to be learned. To effectively learn the strategy, we employ two sensing criteria from the imitation learning perspective: Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). These criteria enable us to imitate the jammer's strategy based on collected interaction samples. Our theoretical analysis indicates that GAIL provides more accurate strategy sensing performance, while BC offers faster learning. Experimental results corroborate these findings. Additionally, empirical evidence shows that our trained anti-jamming strategies, informed by either BC or GAIL, significantly outperform existing intelligent anti-jamming strategy learning methods in terms of sample efficiency.