Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system, an interference decision method based Q-learning algorithm is proposed. First, dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces. Second, a multidimensional motion space is utilized, and the search range of which changes with the distance of the projectile, to select parameters and minimize the amount of ineffective interference parameters. The interference effect is determined by detecting whether the fuze signal disappears. Finally, a weighted reward function is used to determine the reward value based on the range state, output power, and parameter quantity information of the interference form. The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous. The optimal interference form for each distance state has been obtained. Compared with the single-interference decision method, the proposed decision method can effectively improve the success rate of interference.