Transferable Deep Reinforcement Learning Method for Cognitive FASAR Anti-Jamming Strategy Generation

Synthetic aperture radar (SAR) plays a crucial role in modern military reconnaissance. However, SAR often suffers from the loss of scene information in imaging results due to aiming frequency jamming. To enhance the active anti-jamming capability of SAR under varying jamming modes, this article proposes a cognitive frequency agile SAR (FASAR) anti-jamming strategy generation method based on action transfer and learning from demonstrations. The proposed method leverages interactive learning to enable the accurate and rapid generation of anti-jamming strategy across different jamming modes. First, the FASAR adversarial scenario is modeled as a Markov decision process, and reinforcement learning is used to solve the problem. A two-stage reward shaping method is introduced to guide FASAR in learning the optimal anti-jamming strategy. Furthermore, leveraging the concept of transfer learning, the interaction experience with a simulated jammer is used to pre-train the FASAR in the target adversarial environment. By utilizing the FASAR in the simulation environment to guide action selection during the initial phase of confrontation in the target environment, the proposed method further accelerates the generation of anti-jamming strategies. Numerical experiments demonstrate that the proposed method significantly improves the generation speed of anti-jamming strategies across jamming modes compared to existing methods. Moreover, it maintains stable anti-jamming performance throughout the entire confrontation cycle, effectively enhancing cognitive FASAR's initial battlefield environment reconnaissance capability and improving the anti-jamming efficiency of FASAR systems.